3883results about How to "Realize regulation" patented technology

The invention provides a power distribution network automatic operation and maintenance method and system based on artificial intelligence, and relates to the technical field of power operation and maintenance, and the method comprises the steps: collecting the operation data of a power distribution network in real time through a distributed sensor network, transmitting the collected operation data to a cloud data center, and designing a multi-agent reinforcement learning framework, a power distribution network is divided into a plurality of sub-regions, and an intelligent operation and maintenance agent makes a personalized fault processing scheme by monitoring and analyzing a digital twin model in real time, and makes an optimal execution plan based on the fault processing scheme in combination with real-time road condition information and the position of maintenance personnel; and issuing the optimal execution plan to the automation equipment and a mobile terminal of a maintainer through a security encryption channel, extracting preventive maintenance rules from historical operation and maintenance records, and making a preventive maintenance plan based on the extracted preventive maintenance rules in combination with a pre-trained equipment life prediction model.

The invention discloses a multi-modal perception humanoid robot action adaptive control method and system, and belongs to the technical field of computers, and the method comprises the steps: obtaining first multi-modal data; the first multi-modal data comprises first image data, first point cloud data and first force sense data; performing data fusion on the first image data and the first point cloud data, and constructing a three-dimensional model of the scene in real time; generating an optimal path according to the real-time environment data of the three-dimensional model, converting the optimal path into a joint instruction of the humanoid robot, and generating an action control strategy; according to the action control strategy, arm movement of the robot is adjusted through visual servo control, and first operation data are obtained; the path and action of the robot are adjusted in a closed-loop control mode, and second operation data are obtained; and reinforcement learning and regular evaluation are carried out through a DDPG algorithm, and an action control strategy is continuously optimized. According to the invention, multi-modal sensing robot motion adaptive control with strong adaptive capability, high operation precision and good robustness can be realized.

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention discloses a task integrity judgment method for multi-device cooperative work under complex constraint conditions. The method comprises the steps that a comprehensive data collection system is established, multi-dimensional data of cooperative devices are collected in real time and preprocessed, constraint modeling is combined, and the complex constraint conditions are quantified into a computable constraint condition model; a multi-agent collaborative evaluation model is constructed, each device participating in collaborative work is regarded as an agent by the multi-agent collaborative evaluation model, and evaluation of the integrity of the whole task is achieved by simulating the collaborative process between the devices; automatically extracting advanced features from the multi-dimensional data by using a deep learning technology; and a distributed real-time analysis system is designed, and large-scale data processing and continuous model optimization are realized in combination with an adaptive optimization mechanism. According to the method, the reliability and efficiency of the cooperative task can be improved, powerful support can be provided for equipment task decision making, and the method plays an important role in improving the intelligent level of application guarantee and the decision making accuracy.

The invention provides a source-network-storage integrated power distribution network operation optimization method, which relates to the technical field of power distribution networks and comprises the steps of setting an intelligent sensor, collecting multi-dimensional power distribution operation parameters, performing semantic extraction and automatic labeling, determining logic association after conflict correction, obtaining a power distribution knowledge graph, constructing a dynamic prediction model and performing power distribution index prediction. Obtaining a prediction result and generating a key index prediction map; constructing a multi-temporal-spatial-scale collaborative optimization model, setting an objective function, constructing a physical optimization sub-model and a market game optimization sub-model, carrying out interactive coupling, and carrying out collaboration, interactive feedback and iterative convergence to obtain a multi-scale collaborative optimization scheme; and carrying out distributed evaluation, generating an evaluation result, if the evaluation result does not reach the standard, packaging the multi-temporal-spatial-scale collaborative optimization model into an optimization contract, triggering the optimization contract, generating a candidate optimization scheme, carrying out repeated evaluation until the standard is reached, and taking the candidate optimization scheme as a consensus scheme and executing the candidate optimization scheme.

The invention relates to the technical field of communication optimization, in particular to a wireless communication method and system of a vehicle diagnosis system. The method comprises the following steps: identifying a plurality of signal sources in an environment and three-dimensional space data of a current position of a vehicle; performing scene interference intensity comprehensive evaluation on the plurality of signal sources in the environment to obtain a current scene interference intensity evaluation value of the vehicle; environment obstacle space layout analysis is carried out on the three-dimensional space data of the current position of the vehicle, environment three-dimensional modeling is carried out, and a vehicle environment three-dimensional point cloud model is constructed; signal multi-path propagation evolution is carried out according to the current scene interference intensity evaluation value of the vehicle and the vehicle environment three-dimensional point cloud model, and optimal signal frequency band analysis is carried out, so that optimal signal frequency band parameters are extracted; and carrying out dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameter, and constructing a dynamic signal transmission strategy. According to the invention, efficient and stable wireless communication is realized, and the vehicle-mounted communication performance is improved.

The invention relates to the technical field of network time delay scheduling, and discloses a deterministic network time delay scheduling method and device based on a TSN and a storage medium. The method comprises the following steps: carrying out graph theory analysis on a TSN network topology structure to obtain a network node connection relationship and link bandwidth information; extracting time delay requirements and bandwidth requirement characteristics of the data streams, and generating a data stream priority queue and a resource requirement index matrix; inputting a deep Q network, establishing a state-action mapping relation including a network load state and a time slice distribution action, and generating an initial time delay scheduling strategy model; calculating queuing time delay, transmission time delay and processing time delay of each data stream, and generating a time slice resource redistribution scheme; generating a global resource allocation matrix based on the time slice resource reallocation scheme; and parameter feedback updating is carried out on the initial time delay scheduling strategy model to obtain a target time delay scheduling strategy model, and the utilization efficiency of network resources is remarkably improved through bandwidth borrowing and time slice reallocation.

The invention discloses a method for preparing a single-layer tungsten diselenide nanoband based on a space confinement strategy. By changing the flow and the growth time of hydrogen, effective control on the width and the thickness of a nanoband can be realized. The method comprises the following steps: uniformly spraying tungsten trioxide powder to one substrate, then covering the substrate by the other substrate, and forming micro reaction space which is of a 'sandwich'-like structure. Growth of a tungsten diselenide nanoband is carried out by utilizing a constant-pressure chemical vapor deposition method; physicochemical characteristics of a one-dimensional transition metal chalcogenide strongly depend on suspension key types on the edge of the one-dimensional transition metal chalcogenide, metallic properties are displayed by an ultranarrow band, and conversion from the metallic properties to semiconductor properties can be displayed from the edge to the center of a wider band; the tungsten diselenide nanoband is widely applied to micro-nano photoelectric devices due to the special property; industrial production of the tungsten diselenide nanoband can be realized through an experimental method of the invention.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an inspection unmanned aerial vehicle flight control management method, which comprises the following steps: step 1, an unmanned aerial vehicle obtains environment three-dimensional point cloud data through a laser radar, captures terrain visual information through an RGB camera and a depth camera, obtains real-time position and attitude information of the unmanned aerial vehicle through a GPS and an inertial measurement unit, and obtains the real-time position and attitude information of the unmanned aerial vehicle; and fusing the collected data to construct a spatial grid map, and taking the grid map as input data of path planning. A high-precision space grid map is generated through three-dimensional environment perception and modeling in combination with an unmanned aerial vehicle multi-sensor fusion technology, and a path is updated in real time by using dynamic environment data, and an air route is planned and optimized, so that path dynamic adjustment and energy consumption minimization of the unmanned aerial vehicle in a complex dynamic environment are realized; and the effects of improving the task execution efficiency, reducing the flight energy consumption and improving the flight safety are achieved.

The invention relates to the technical field of intelligent spectrum regulation and control, and discloses an intelligent spectrum regulation and control system based on multi-modal data fusion and dynamic optimization, which comprises a multi-modal data acquisition module, a real-time data processing module, a dynamic weight distribution algorithm, a deep reinforcement learning regulation and control algorithm and a distributed collaborative optimization module. According to the system, real-time fusion and dynamic priority adjustment of multi-dimensional data are realized through acquisition of multi-modal data (environment spectrum, temperature and humidity, carbon dioxide concentration and user behavior) and dynamic weight distribution based on LSTM (Long Short Term Memory); dynamic optimization of a spectrum regulation and control strategy is realized by combining a deep reinforcement learning model and utilizing a weighted reward function of a spectrum output error and energy consumption, and efficient light supplement and low energy consumption requirements are balanced; a distributed optimization algorithm with edge computing and cloud collaboration is adopted, computing resources are dynamically allocated, task processing efficiency is greatly improved, and system power consumption is reduced.

The invention relates to the technical field of equipment remote control, and discloses a remote control method and system for sewage treatment equipment and a storage medium. The method comprises the following steps: performing communication encryption and identity authentication processing on a standard data packet of a sewage treatment system, and constructing an equipment communication link; performing intelligent analysis and state diagnosis processing on the data in the equipment communication link, and generating an equipment operation state report; performing control strategy generation and parameter matching processing according to the equipment operation state report, and outputting an equipment control instruction; performing hierarchical analysis and execution state monitoring on the equipment control instruction, and establishing a control feedback data link; and performing remote optimization treatment according to the data in the control feedback data link so as to realize remote intelligent control of the sewage treatment equipment. Under the condition that the sewage treatment equipment is dispersedly arranged, the real-time monitoring of the running state of the equipment and the intelligent adjustment of the process parameters are realized, and the stability of the treatment effect is ensured.

The invention provides a real-time data monitoring system in a knitted fabric production process, and relates to the technical field of data monitoring, and the system comprises a real-time monitoring module which is used for directly collecting actual production data from equipment and the environment of a production workshop through various sensors; the data analysis module is used for comparing the initial prediction value of the prediction model with actual production data to output final prediction data and generating optimization parameters according to the final prediction data; the prediction production module is used for establishing a virtual production model according to the actual production data, and the prediction model predicts an initial prediction value of the output variable based on the input data; and the dynamic adjustment module is used for generating a dynamic deviation threshold value according to the deviation trend data and generating a corresponding adjustment strategy according to whether the deviation value falls into the deviation threshold value or not. The precision and stability of the production process are improved, and the influence of human factors is reduced.

The invention relates to the technical field of light source configuration, and discloses a semiconductor wafer defect detection light source configuration method, device and equipment, and the method comprises the steps: obtaining a semiconductor wafer image, and carrying out the dynamic image convolution processing and defect feature enhancement to obtain an enhanced defect feature image; inputting the enhanced defect feature map into a two-way deep neural network for reflected light intensity calculation and image quality scoring to obtain reflected light intensity data and image quality scoring data of the defect region; acquiring light source input current and light intensity sensor data of a plurality of light sources, and performing identification calculation on the light source angle parameters in combination with the image quality scoring data to obtain light source initial parameters; performing nonlinear control based on the reflected light intensity data and the light source initial parameter to obtain a light source control parameter; according to the method, the accuracy and the reliability of defect detection are improved, and the manual debugging cost of light source parameter configuration is reduced.

The invention provides an intelligent access method and system for small accessories of an unmanned aerial vehicle, and relates to the technical field of intelligent access. Generating an action feature vector through an action semantic understanding model; identifying key action nodes and generating a basic action sequence; constructing an action semantic mapping model; performing mapping analysis based on the execution equipment capability; generating an equipment control instruction; collecting action execution state data; establishing an execution evaluation model; generating a compensation instruction and updating the control instruction; and intelligent access operation is completed. According to the method, intelligent storage and taking of the unmanned aerial vehicle accessories are achieved, and the operation efficiency and precision are improved.

The invention discloses an intelligent data acquisition system of a special pump, and relates to the technical field of equipment management. Comprising a data acquisition module, a data preprocessing module, a data storage module, a data analysis and prediction module, an intelligent control module, a remote monitoring module, a maintenance and optimization module, a multi-source data fusion module, a digital twinborn simulation module and a collaboration module. According to the intelligent data acquisition system of the special pump, by arranging multi-dimensional data acquisition and an intelligent optimization model based on the self-adaptive neural network and reinforcement learning, the defects that in an existing system, data integration is not comprehensive, a maintenance decision is lagged and intelligent optimization is lacked are overcome, and real-time monitoring and dynamic adjustment of the equipment state are achieved. The system can accurately predict the equipment fault, optimize the maintenance strategy, significantly reduce the operation cost, and improve the operation efficiency and reliability of the equipment.

The invention relates to the technical field of expressway unmanned aerial vehicle route planning, in particular to an intelligent route planning method and system for an expressway unmanned aerial vehicle, and the method specifically comprises the steps: planning unmanned aerial vehicle waypoints on an expressway based on a target three-dimensional model, and obtaining a waypoint set of the expressway; historical route data of the expressway are obtained, a deep learning algorithm is used for analyzing the historical route data, safe flight path features are extracted, a route safety evaluation model is constructed, and the route safety evaluation model is used for evaluating the route safety level of the route of the unmanned aerial vehicle; and determining route planning parameters according to the unmanned aerial vehicle task type, and determining an unmanned aerial vehicle route through the waypoint set, the route safety evaluation model and the waypoint planning parameters based on a multi-objective optimization algorithm. According to the invention, the safety and efficiency of the unmanned aerial vehicle in highway inspection and monitoring tasks are improved, and automatic planning and dynamic adjustment of the route of the unmanned aerial vehicle based on real-time data analysis are realized.

The invention discloses a complex three-dimensional scene twinborn modeling method guided by a geographic knowledge map, belongs to the technical field of digital twinborn, and solves the problems that the existing twinborn scene modeling method mostly focuses on single space dimension fusion and does not consider the multi-dimension fusion requirement, such as the synergistic effect of objects, attributes and the whole scene is not considered, and the modeling effect is poor. The model precision is reduced, and the adaptability is insufficient. The method comprises the following steps: constructing a scene-feature-transition complex three-dimensional scene geographic knowledge graph based on obtained dynamic and static data of each geographic scene in a complex three-dimensional twinborn scene physical space, and updating in real time; and carrying out dynamic fusion modeling on the complex three-dimensional scene through progressive guidance layer by layer from an object level, an attribute level to a scene level by adopting a geographic knowledge graph of the complex three-dimensional scene. The method is used for complex three-dimensional scene twin modeling.

The invention relates to the technical field of dynamic migration, in particular to an edge computing management system supporting dynamic migration of an AI model. The system comprises a state mining module used for identifying available edge computing devices; performing multi-frequency information monitoring on the available edge computing devices, and performing device comprehensive state mining to generate edge device comprehensive state features; the edge topology module is used for carrying out node position positioning one by one based on edge equipment comprehensive state characteristics and carrying out instant state mapping modeling so as to construct an edge computing node topology model; the task flow trend module is used for acquiring an AI model task running log; and performing task queue analysis on the AI model task running log, and then performing model task traffic trend analysis to obtain AI model task traffic trend data. According to the invention, edge computing management with quick response and high processing efficiency is realized.

The invention discloses a data cooperative transmission method and system for a low earth orbit satellite Internet of Things terminal, and the method specifically comprises the steps: carrying out the classification cutting of environment data, and forming a plurality of environment data subsets; determining a plurality of cooperative sending terminals and respective optimal communication paths between the first low earth orbit satellite Internet of Things terminal and each cooperative sending terminal based on a communication topological structure relationship between the low earth orbit satellite Internet of Things terminals in the ad hoc network; transmitting the environment data subsets with different priorities to corresponding cooperative sending terminals through different optimal communication paths; and according to the environment data subsets sent by each cooperative sending terminal and received by the low-orbit satellite, splicing the environment data subsets and forwarding the spliced environment data subsets to a target terminal or a ground station or a data center. According to the invention, the cooperative effect among a plurality of low-orbit satellite Internet of Things terminals can be fully utilized, and stable and reliable data transmission under the requirements of large data volume and high real-time performance is realized.

The invention relates to the technical field of brushless motor testing, and discloses a test adjusting method and system for an intelligent brushless motor, and the method comprises the steps: carrying out the real-time monitoring of the current, rotating speed and load characteristics of the brushless motor, obtaining the operation data of the motor, and obtaining a motor working mode recognition result; constructing a motor parameter hierarchical optimization scheme; performing dynamic optimization to obtain a motor adaptive control parameter set; performing rolling optimization solution to obtain a motor prediction control instruction sequence; performing multi-objective optimization calculation to obtain a motor multi-performance balance adjustment scheme; a motor multi-performance balance adjustment scheme is integrated into a digital twin test platform, simulation verification is carried out on the performance of the brushless motor under different working conditions, a motor test adjustment strategy is obtained, the working mode of the motor can be recognized in real time, parameter optimization and performance prediction can be carried out based on historical data and the current state, and the stability of the motor test adjustment strategy is improved. Therefore, the optimal operation state of the motor under different working conditions is realized.

The invention relates to the technical field of gas energy monitoring, and discloses a gas energy monitoring method based on Internet of Things, comprising the following steps: S1, multi-source data acquisition: using an intelligent sensor to acquire gas related data, and sending the data to an edge device or a cloud platform through a wireless network; s2, data preprocessing: carrying out data cleaning, denoising and feature extraction on edge equipment, executing multi-source data fusion, carrying out anomaly detection and processing, and filtering abnormal data; and S3, data transmission: performing data transmission by adopting an encrypted communication protocol, and using a data buffering and retransmission mechanism in the transmission process, the data buffering and retransmission mechanism being used for ensuring data integrity. By using a multi-source data fusion technology and an advanced data preprocessing algorithm, sensor noise and data errors are effectively reduced, the quality and consistency of gas data are remarkably improved, and the problem that the analysis accuracy is reduced due to data source diversity and complexity is solved.

The invention belongs to the technical field of collaborative robots, and particularly discloses and provides an anti-explosion mechanical arm positive pressure control system and method.The system comprises a mechanical arm motion tracking module, an environment data monitoring module, a configuration adjustment analysis module, a pressure adjustment analysis module, a database and a pressure adjustment execution terminal. The mechanical arm motion tracking module is designed to track the motion state of the mechanical arm and record motion data, a key part pressure sensor is started to monitor pressure data, and the correlation change of motion and pressure can be captured. The environment data monitoring module monitors environment parameters of a working area in real time, and environment changes are comprehensively mastered. The all-dimensional monitoring capability of the system is improved, factors influencing positive pressure control can be obtained more comprehensively, meanwhile, configuration adjustment judgment is carried out in combination with environment, movement and pressure data, adjustment values are dynamically determined, and intelligent decision making and self-adaptive adjustment of mechanical arm positive pressure control are achieved.

The invention relates to the technical field of camera adjustment, in particular to a camera adaptive adjustment method and system and a storage medium. The method comprises the following steps: acquiring a multi-source environment sensing data set and an initial environment image; performing environment feature recognition on the initial environment image according to the multi-source environment sensing data set to obtain a scene environment feature map; performing semantic scene segmentation on the scene environment feature map to obtain a semantic scene macroscopic feature map; performing time sequence attention target tracking on the scene environment feature map to obtain a target vehicle motion feature map; performing feature constraint fusion on the semantic scene macroscopic feature map and the target vehicle motion feature map to obtain a target vehicle-scene motion semantic map; and performing parameter adjustment on the camera according to the target vehicle-scene motion semantic map to obtain a camera parameter adjustment strategy set. According to the invention, the performance and the image quality of the camera in a changeable environment are improved.

The invention relates to the technical field of automatic loading and logistics control, in particular to an automatic powder loading device based on visual identification, which comprises a visual identification module, a flexible grabbing module, a transfer module and a stacking module, the form, internal distribution and material characteristic data of the powder bag are obtained through a multi-mode sensor, a grabbing path is generated, and grabbing operation is completed through a flexible manipulator; the transfer module dynamically adjusts the transportation state of the powder bag in combination with path optimization and posture monitoring; the stacking module monitors stress of a stacking layer in real time on the basis of the stacking thermal distribution diagram, adjustment suggestion data are generated, stacking position and angle optimization is completed through a self-correction unit, and the stability of a stacking structure is ensured; the powder loading efficiency, the stacking precision and the long-term stability are remarkably improved, and the method is suitable for efficient logistics management of complex loading scenes.

The invention relates to the technical field of data processing, and discloses a communication transmission management method and system for a heterogeneous network and a storage medium. The method comprises the following steps: processing a signal strength parameter and a data priority level in a data hierarchical configuration table to obtain a network resource allocation scheme; performing real-time monitoring processing on a 4G / 5G cellular network base station, a WiFi hotspot and a satellite communication node to obtain a network switching control instruction; performing transmission quality monitoring processing on the engine parameters, the navigation data, the airborne equipment state data and the environment monitoring data to obtain a transmission quality parameter table; and performing data mining analysis processing on the network switching control instruction and the transmission quality parameter table to obtain a transmission strategy optimization parameter containing network resource optimization configuration. In a heterogeneous network environment, intelligent network switching and resource allocation based on data priorities are realized, so that transmission requirements of data with different priorities are ensured, and meanwhile, the utilization efficiency of network resources is improved.

The invention relates to the technical field of data processing, and discloses an operation mode switching control method and device for an offshore wind turbine generator. The method comprises the following steps: carrying out model classification on historical operation data of a unit to obtain operation mode classification data and a characteristic parameter library; performing reinforcement learning analysis on the current state evaluation data and the short-term prediction data of the unit to obtain a target operation mode; performing fuzzy control processing on the target operation mode to obtain a mode switching strategy and a parameter adjustment sequence; performing multi-objective optimization and cooperative control analysis on the mode switching strategy and the parameter adjustment sequence to obtain accurate control signals of each actuator in the target offshore wind turbine generator; and generating an operation mode switching control strategy according to the accurate control signal of each actuator, and performing mode switching control on the target offshore wind turbine generator through the mode switching control strategy. According to the invention, the efficiency and accuracy of the operation mode switching control of the offshore wind turbine generator are improved.

The invention relates to the technical field of computer television image communication, in particular to a computer television all-in-one machine control method, system and device. The method comprises the following steps: through multi-view image acquisition and processing, generating a three-dimensional scene grid and carrying out scene element analysis to form a scene element set, then chasing limbs of a user, generating a user limb image set, restoring an environment basic tone, obtaining display content of the all-in-one machine and carrying out content semantic analysis, and finally obtaining a user limb image set. Calculating interaction intensity to obtain content interaction intensity data, speculating a user body language and performing user intention prediction in combination with the interaction intensity data, adjusting display content of the all-in-one machine based on the predicted user operation intention, generating simulation adjustment content, performing composition optimization through a scene element set, and generating updated display content. The method for controlling the computer and television all-in-one machine is more accurate and more efficient.

The invention discloses an intelligent metasurface channel optimization method and system based on a beam forming algorithm, and relates to the technical field of intelligent metasurfaces, and the method comprises the steps: collecting real-time environment data, monitoring channel state data, and evaluating the influence of the environment and the channel state on a signal; evaluating the gain strategy of the intelligent metasurface, and evaluating the performance of the current beam forming strategy by combining the influence of the environment and the channel state on the signal; and calculating an optimal beam forming parameter by using a gradient descent method to realize channel optimization. According to the method, through comprehensive utilization of real-time environment monitoring, channel state evaluation, an advanced data analysis technology and a mathematical optimization method, dynamic adjustment and real-time optimization of intelligent metasurface channel optimization are achieved, efficient and stable operation of a communication system in a complex environment is ensured, the speed and reliability of data transmission are remarkably improved, and the method is suitable for popularization and application. The invention provides a more efficient, flexible and adaptive beam forming strategy optimization method, and the performance of a wireless communication system is remarkably improved.

The invention provides a virtual resource scheduling method and apparatus, an electronic device, a storage medium and a product. The method comprises the steps of obtaining current performance index data of a virtual machine; inputting the current performance index data of the virtual machine into a virtual machine resource load prediction model to obtain first prediction performance index data of the virtual machine; wherein the virtual machine resource load prediction model is obtained by performing fine tuning training on a pre-trained large language model based on historical performance index data of a virtual machine; and performing virtual resource scheduling based on the first prediction performance index data of the virtual machine. According to the method, virtual resource intelligent dynamic scheduling based on a large language model fine tuning technology can be realized, the problem that a traditional prediction model is obviously insufficient in perception capability of virtual machine environment characteristics and business scene characteristics is solved, and the identification and prediction capability of a virtual machine resource working mode is obviously improved; moreover, the method can adapt to a dynamic environment, and achieves the periodical and real-time adjustment and optimization of virtual machine resources, thereby guaranteeing the efficient utilization of the resources.