2209 results about "Scheduling system" patented technology

The invention provides a digital twinning-based stereoscopic warehouse goods allocation and sorting scheduling method and system, and relates to the technical field of digital twinning, and the method comprises the steps: constructing a three-dimensional digital model of a stereoscopic warehouse; goods allocation is carried out based on a deep reinforcement learning algorithm, the goods access frequency, the associated purchase probability and the seasonal demand prediction are used as input parameters, and a goods allocation instruction is generated by taking the shortest sorting path and the associated goods centralized storage as optimization targets; warehousing operation is executed, and a sorting order is received; constructing a virtual potential field in the model, and when the repulsive force between stackers exceeds a threshold value, determining a task priority based on the order emergency degree and triggering obstacle avoidance; and calculating an obstacle avoidance track and generating a cooperative scheduling instruction to execute picking operation. According to the invention, efficient goods allocation and intelligent multi-stacker collaborative scheduling are realized.

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention relates to the technical field of computing power scheduling, provides a distributed computing power scheduling method and system, breaks through the limitation that a traditional computing power scheduling system depends on a single index or a static strategy by constructing an integrated architecture of data perception, load prediction and intelligent scheduling, and constructs a self-adaptive and closed-loop optimized intelligent scheduling system. The scheduling system obtains multi-source data in real time and fuses the multi-source data into a perception vector to accurately describe the running state of the system, accurate pre-judgment of load changes is achieved through hierarchical load prediction, and resources can be efficiently allocated in different scenes in combination with double scheduling paths and execution feedback optimization; according to the scheduling system, the resource utilization rate of the distributed system is effectively improved, the energy consumption cost is reduced, the task execution delay is reduced, the system stability and the fault-tolerant capability are enhanced, and an innovative computing power scheduling solution is provided for a large-scale distributed computing scene.

The invention relates to the field of artificial intelligence, discloses a large language model driven complex manufacturing and production scheduling method and system, and aims to solve the problems that a traditional scheduling system depends on a static rule, is difficult to cope with multi-constraint dynamic disturbance, is weak in semantic understanding ability and is poor in execution interpretability. The method comprises the steps that a special large language model base is constructed and manufactured, and work orders, equipment, materials, processes and abnormal event data are packaged in a unified mode through a semantic collection module; a task intention recognition sub-module, a resource matching sub-module and a time sequence conflict detection sub-module are used for jointly analyzing the semantic unit; and the scheduling engine based on reinforcement learning generates an optimal action sequence under the multi-target weighting constraint. According to the method, minute-level high-dimensional scheduling, anti-disturbance re-planning and man-machine cooperative execution are realized through semantic driving and dynamic evolution architecture, the equipment efficiency is remarkably improved by more than 15%, the delivery delay is reduced by 30%, the line change loss is reduced by 20%, and intelligent manufacturing is promoted to evolve from rule driving to semantic self-adaption.

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The invention discloses a multi-robot task conflict resolution and dynamic scheduling system and method, and belongs to the technical field of robot control. The system comprises a perception detection layer which is used for acquiring operation information of a plurality of robots and a space-time semantic map of a to-be-executed task executed by the robots, and generating conflict information under the condition that at least two target robots are detected to conflict; the decision scheduling layer is used for determining task execution priorities of the to-be-executed tasks according to the conflict information and priority factors and value functions of the to-be-executed tasks corresponding to the target robots so as to rearrange the to-be-executed tasks corresponding to the target robots and generate a task sequence; and the execution control layer is used for generating a dynamic scheduling instruction according to the task sequence, the space-time semantic map and the operation information of the target robot and issuing the dynamic scheduling instruction to the corresponding target robot. The system can flexibly cope with a dynamic scheduling scene of multiple robots in real time.

The invention relates to the field of electric power material quality detection, and discloses a test scheduling system for detection task collaboration and data acquisition, which comprises a task construction module, a state collaboration module, a graph reasoning module and a data acquisition module. And the task construction module generates a standardized test task packet including a task identifier, a project code, a target equipment identifier, an environment requirement parameter and a two-dimensional code according to the test rule base and the resource configuration state, and pushes the standardized test task packet to corresponding test equipment through a Web Service interface. And the state collaboration module receives an equipment state feedback event, constructs an event time sequence flow graph based on the task identifier and generates a task state sequence with a timestamp. The atlas reasoning module takes the state sequence and the environmental parameters as input, constructs a test atlas structure and generates an optimization execution path. And the data acquisition module controls the test equipment to complete a detection task according to the path, acquires test data and environmental parameters, and encapsulates the test data and the environmental parameters to form a structured task data packet, thereby realizing data collection and task tracing.

The invention discloses a cloud side-end cooperative task scheduling and efficiency optimization method and system for heterogeneous patrol resources, and relates to the technical field of intelligent scheduling and resource optimization. According to the method, accurate perception of a resource state is realized by constructing a digital twinborn and federated learning mechanism, resource contention conflicts are solved by adopting a space-time diagram attention network and multi-agent reinforcement learning, and multi-target optimization and trusted execution are realized in combination with a quantum genetic algorithm and a block chain smart contract. Finally, the stability of the system is verified through Lyapunov optimization, a complete scheduling system from resource perception and conflict resolution to steady state maintenance is formed, and the task scheduling efficiency and the system stability in the heterogeneous resource environment are remarkably improved.

The invention discloses an intelligent port cargo scheduling system and method based on the Internet of Things, relates to the technical field of intelligent port logistics scheduling, and realizes dynamic adaptive adjustment of a port scheduling strategy by constructing a full-chain linkage mechanism of real-time sensing, intelligent analysis, dynamic decision, accurate execution and closed-loop feedback. The system can automatically expand or contract the boundary of the storage yard buffer area according to the congestion index of the core operation area and ship tide prediction, and synchronously adjust the AGV and the yard bridge scheduling rule, in the peak tide period, the AGV preferentially guarantees the rapid transfer of cargoes in the core area, the efficiency loss caused by long-distance transportation is avoided, and the transportation efficiency is improved. In the low ebb backflow period, goods in the elastic storage area are actively moved to the core preparation area in advance, layout is conducted in advance for the follow-up operation peak, the dynamic scheduling mode can effectively cope with severe fluctuation of the port operation amount, all the operation links are kept efficient and cooperative all the time, and the adaptability and response capacity of the port in the complex operation environment are greatly improved.

The invention discloses a reservoir real-time scheduling simulation system based on a deep learning algorithm, and belongs to the technical field of intelligent water conservancy and artificial intelligence. Aiming at the problems of low prediction precision, poor multi-target coordination capability, weak coping uncertainty and the like of a traditional scheduling system, the system is designed to acquire hydrological, meteorological, water quality and engineering safety data through a multi-source data acquisition unit, and a multi-dimensional feature tensor is generated after preprocessing and fusion; the dispatching center server adopts an STGCN-LSTM mixed model to achieve high-precision prediction and uncertainty quantification of the water inflow process in the future 7-30 days, a reservoir hydrodynamic model and an MO-PPO algorithm are combined to complete multi-scene simulation and multi-target optimization decision, and an AF-DT mechanism dynamically adjusts the dispatching rule priority. According to the system, a sensing-decision-execution-feedback closed loop is constructed, the scheduling adaptive capacity and robustness are improved, the synergistic interaction of flood control, water supply, power generation and ecological protection is realized, and the system is suitable for real-time intelligent scheduling of large and medium reservoirs.

The invention discloses a quantum classical hybrid computing task dynamic scheduling system and method based on SLURM. The system comprises a quantum task feature extraction module, a dynamic priority evaluation module, a dependency analysis module, a quantum perception backfilling module and a uniform resource abstraction layer module. The method comprises the following steps: extracting quantum features of a to-be-processed task and a computing resource to form a quantum feature set and caching the quantum feature set; the feature set is obtained in real time, task priorities are output through multi-dimensional evaluation, and a real-time priority sequence is generated; based on the task type and the feature set, forming a dependency relationship between the classic task and the quantum task, and converting the dependency relationship into a dependency constraint and / or resource reservation instruction; according to the task priority, the resource reservation instruction and the real-time resource state, a future idle period is predicted, and a short-time quantum task is inserted for backfilling; computing resources are distributed according to the task priority and the dependency constraint, and the resource utilization state is fed back to the backfill and priority evaluation module in real time. According to the method, efficient scheduling of hybrid computing tasks can be realized, and the overall performance of the system is remarkably improved.

The invention discloses an intelligent resource scheduling system and method based on an elastic threshold value and AI prediction, and relates to the technical field of computer resource management. For the limitation of the existing resource scheduling method, the provided scheme comprises an elastic threshold configuration module used for defining performance indexes of computing resources, allocating weights and setting initial upper and lower limits of an elastic threshold; the dynamic threshold value generation module is used for collecting and calculating resource performance index data in real time, calculating a real-time dynamic threshold value after preprocessing, and comparing the real-time dynamic threshold value with an elastic threshold value range; the AI prediction module is used for training and optimizing a prediction model, and the model obtains a future computing resource demand prediction result based on the new data; and the real-time scheduling engine module is used for formulating a resource scheduling strategy, distributing computing resources, executing computing resource increasing and decreasing operation, monitoring a scheduling result and feeding back the scheduling result, so that the modules are adjusted, and a dynamic optimization closed loop of the resource scheduling strategy is formed. The method is used for improving the computing resource utilization rate.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

The invention discloses a deep and large reservoir ecological scheduling method, system and equipment based on a data-driven model of a coupling physical mechanism, and belongs to the technical field of water resource management and environmental protection. Firstly, a physical water temperature model is constructed based on measured data, diversified water temperature change scenes are generated, and a deep learning model constrained by a physical mechanism is constructed. Secondly, carrying out sensitivity analysis to identify key influence factors for driving water temperature change, constructing a reservoir optimization scheduling model, coupling a deep learning model constrained by a physical mechanism, and deducing a scheduling rule set on the premise of meeting a water temperature target; and finally, carrying out multi-index optimization analysis. The invention further provides a deep and large reservoir ecological scheduling system and electronic equipment, and the deep and large reservoir ecological scheduling method is realized. According to the method, the power generation scheduling rule set of the deep and large reservoir can be scientifically deduced, the optimal scheduling scheme with both ecological benefits and economic benefits is screened out by introducing the multi-index optimization method, and overall balance of ecological requirements and power generation benefits is achieved.

The invention relates to the technical field of integrated circuits and computer networks, and particularly discloses a transmission control and intelligent scheduling system for an integrated chip, and the system sets a dual-mode decision and dynamic switching mechanism for each routing node. Calculating dynamic characteristic parameters including an instantaneous value, a first-order trend and a second-order acceleration; when the parameter is matched with a pre-stored abnormal feature set and the load exceeds a threshold value, the node is immediately atomized and switched to a predefined security scheduling strategy loaded from a shared storage area, and otherwise, the node generates a scheduling decision according to self-adaptive decision logic continuously optimized based on historical performance feedback; all the nodes carry out data packet forwarding control according to the current execution strategy; and the system also periodically realizes federated global knowledge evolution according to the quality evaluation result of the self-adaptive decision of each node.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention discloses an LLM central multi-agent tool arrangement and virtual-real closed-loop evolution scheduling system. The system comprises an intelligent sensing module, a knowledge module, an intelligent scheduling decision module and a closed-loop execution module. The intelligent sensing module collects real-time and multi-modal data of a physical environment and outputs structured sensing data, the knowledge module is used for constructing a knowledge graph and a historical task case library, and the intelligent scheduling decision-making module takes a large language model (LLM) as a core scheduling center, receives and analyzes task instructions, fuses the structured sensing data and graph and case information, and performs task scheduling on the knowledge graph and the historical task case library. Generating a decision scheme according with a constraint condition by using knowledge enhanced reasoning, and continuously optimizing in multiple iterations to generate a decision instruction; and the closed-loop execution module receives and drives the execution end to execute the decision instruction. According to the method, an intelligent closed-loop system of perception-decision-execution-evolution is constructed, and global optimization and high-reliability self-adaptive scheduling of multi-machine collaborative welding are realized.

The invention provides a multi-dimensional driver capability evaluation and intelligent matching scheduling system, and the system comprises a data collection module which is used for obtaining driver driving behavior data, vehicle state data and environment data in real time; the preprocessing module is used for carrying out noise filtering, missing value filling and standardization on the acquired data; the multi-dimensional capability evaluation module is used for calculating a driving safety score, an efficiency score and an emergency response score of the driver through a dynamic weight distribution algorithm based on the preprocessed data; the demand analysis module is used for analyzing the route complexity, the time sensitivity and the special service demand of the passenger order; the matching scheduling module is used for generating a matching result according to the driver ability score and the passenger demand and outputting a scheduling instruction; the dynamic optimization module monitors the driver state and the road condition change in real time and adjusts the matching weight; and the interaction module is used for pushing real-time scheduling information and abnormal event early warning to the driver and the passenger. The scheduling efficiency and safety can be improved, and the passenger travel experience and the operation management level are improved.

The invention provides a task alarm processing method and system based on intelligent grading, and relates to the technical field of task scheduling alarm, and the method comprises the steps: collecting execution behavior data of each task in a task scheduling system in a plurality of time windows, carrying out the time sequence coding, and converting the execution behavior data into a state transition matrix, identifying an abnormal task and quantifying an alarm intensity value by analyzing non-stationary features; constructing a data flow direction and resource competition relation heterogeneous graph between the tasks, and mapping the abnormal tasks to corresponding nodes; generating a context sensing vector through multi-hop neighborhood aggregation, forming an alarm cluster based on semantic distance clustering, and determining a core node; and determining a control instruction according to the alarm intensity of the core node, performing conflict detection in combination with a resource competition relationship, adjusting a task execution time sequence, and predicting a failure probability output risk type based on a state transition path. According to the invention, intelligent grading and accurate processing of alarms can be realized, and the reliability and resource utilization efficiency of a task scheduling system are improved.

The invention provides a resource collaborative scheduling system and method for a virtual power plant. The method comprises the following steps: determining space-time probability distribution of wind and light output in the virtual power plant through historical meteorological data and historical illumination data; determining space-time load distribution of an electric vehicle cluster in the virtual power plant, and constructing a source-load interaction scene set under multiple space-time scales in the virtual power plant by fusing the space-time probability distribution and the space-time load distribution; determining a multi-objective optimization function of the virtual power plant according to the price demand signal of the electric energy service market and the source-load interaction scene set; and performing optimization solution on the multi-objective optimization function to obtain a collaborative scheduling plan of the virtual power plant, decomposing the collaborative scheduling plan into a control instruction sequence, and issuing the control instruction sequence to a local controller of each distributed resource. According to the scheme of the invention, a multi-target optimization system considering the operation benefit and the renewable energy power abandonment rate can be constructed through the source-load interaction scene under multiple spatial-temporal scales, so that the closed-loop management and control of the resource scheduling of the virtual power plant can be realized.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant optimal scheduling system and method, and the system comprises a data obtaining module, an edge calculation module, a prediction module, a scheduling controller, a topology reconstruction module, and an intelligent terminal device cluster. According to the invention, the edge computing module carries out localization processing and prediction on the sensing data, so that rapid generation and issuing of a scheduling scheme are realized, and the problem of response delay caused by network transmission and centralized computing of a traditional centralized architecture is avoided, thereby supporting millisecond scheduling feedback and improving the scheduling efficiency. The real-time response capability under the sudden load fluctuation or fault condition is remarkably improved, a multi-dimensional perception and prediction mechanism is constructed based on an LSTM neural network prediction model, the recognition and trend prediction capability of the system on meteorological disturbance, equipment aging and operation abnormity is enhanced, the intelligent level of the virtual power plant system is improved, and the real-time performance of the virtual power plant system is improved. The system can dynamically generate an optimal scheduling strategy to ensure stable operation of the virtual power plant under various working conditions.

The invention relates to the technical field of cable laying management, and discloses a power industry cross-domain computing power dynamic scheduling system based on super intelligent fusion. According to the system, computing power demand data of each business domain in the power industry is collected in real time, computing resource demand characteristics in different business scenes are identified, and a cross-domain computing power demand characteristic graph is generated; meanwhile, the real-time load state and the available resource quantity of each computing node are continuously tracked, and a distributed computing power resource state matrix is constructed; analyzing and calculating a matching relationship between resource demands and available resources, and generating a multi-objective optimized computing power scheduling strategy scheme; according to the scheme, power business calculation tasks are distributed to optimal calculation nodes according to priorities and resource requirements, and a cross-domain execution process is triggered; and finally, the task execution state and the resource use condition are monitored in real time, an evaluation report is generated and fed back to a strategy generation module, closed-loop optimization is formed, and efficient collaboration and dynamic scheduling of cross-domain computing power resources in the power industry are achieved.

The invention provides a multi-machine cooperative scheduling system of a man-machine control shelter, and belongs to the technical field of multi-machine cooperative scheduling control. The environment feature intelligent classification and curved surface modeling module is connected with the task semantic understanding and planning module; the task semantic enhancement and risk assessment module is connected with the environment feature intelligent classification and curved surface modeling module; and the multi-machine collaborative decision module is connected with the task semantic enhancement and risk assessment module. An organic whole is formed through tight cooperation of functions such as task semantic understanding, environment modeling, risk assessment and decision making, different types of machines can efficiently and cooperatively complete complex tasks under unified scheduling of the system, the limitation of isolated operation of modules in a traditional system is broken through, and the reliability of the system is improved. And the harsh requirements on multi-machine collaborative operation in multiple fields and multiple scenes are met.

The invention discloses a multi-brand AGV same-field mixed operation management and control method and device, electronic equipment and a storage medium, and the method comprises the steps: building a standardized data channel with an original scheduling system of each brand AGV, so as to obtain the real-time operation state data and planning path data of each AGV; based on the real-time operation state data and the planned path data, carrying out space intersection judgment on planned paths of all AGVs, and carrying out collision risk initial judgment on AGV pairs with space intersection based on preset limit operation parameters; motion simulation is carried out on the AGV pair with the collision risk through preliminary judgment in the virtual environment based on the actual operation parameters so as to output a quantized collision risk result; and based on the quantized collision risk result, generating an avoidance strategy for the AGV with the low priority in the AGV pair with the collision risk in combination with a preset dynamic priority rule, and issuing and executing a standardized scheduling instruction generated based on the avoidance strategy. According to the invention, safe and efficient cooperative operation of multiple brands of AGVs in a same-field mixed operation environment can be realized.

The invention discloses an intelligent-based multi-task self-adaptive scheduling system for a pension accompanying robot, and belongs to the technical field of multi-task self-adaptive scheduling. Comprising a multi-modal task perception fusion module, a dynamic task evaluation module, a dynamic task priority generation module, a quantum causal reasoning module, a quantum optimization task scheduling module, a multi-task collaborative optimization module and a man-machine collaborative interaction module. Task features are mapped to a multi-dimensional weight space, a time attenuation coefficient is introduced to dynamically adjust the influence of historical causal association, a dynamic adjustment mechanism ensures timely execution of high-priority tasks, a quantum superposition state is utilized to parallelly search a global optimal solution through a quantum optimization task scheduling module, and the task scheduling efficiency is improved. Through a dynamic priority queue and a conflict solution feedback mechanism, the scheduling scheme is iteratively optimized, and the feasibility and the resource utilization rate of the scheduling scheme are remarkably improved.

The invention relates to a copper-clad plate production line collaborative regulation and control method based on digital twinning, and the method comprises the steps: constructing a multi-level data set of dynamic virtual-real mapping through full-process digital twinning modeling, process data normalization and multi-level feature tagging, achieving the high consistency of a physical production line working condition and a simulation system, and achieving the collaborative regulation and control of a copper-clad plate production line in combination with an autonomous scheduling intelligent agent. Real-time perception and joint feature mining of multi-dimensional data such as equipment load, health degree and energy consumption are realized, the real-time adaptive scheduling capability of a production line to states such as sudden load and equipment aging is improved, scheduling target weights are periodically and adaptively generated, optimal instant balance of multi-target income is realized, and the optimal real-time scheduling capability of the production line is realized by adopting an evolutionary game and swarm intelligent optimization. A cross-device optimal resource scheduling scheme under the multi-target constraint is obtained, an evolution model is continuously fed back through real-time production data, a scheduling-execution-calibration-rescheduling closed-loop mechanism is formed, and the self-learning and long-period stability capabilities of a scheduling system are remarkably improved.

The invention discloses a security check door security check process optimization scheduling system and method based on big data, and belongs to the technical field of intelligent security check, a whole security check area is divided into a plurality of functional areas and mapped into network nodes according to security check process links and physical space distribution, directed edges among the nodes represent passenger flow paths, and the flow paths of passengers are distributed in the network nodes. A weight matrix of edges is calculated through historical flow data, and a security check area network topology model is constructed; fusing ticket business, historical security check and real-time sensor data, constructing a load evaluation model, and calculating a node load index and an influence conduction coefficient; based on passenger ticket business time and historical behavior records, establishing a time urgency index and risk level model, and forming a two-dimensional decision matrix to dynamically divide priorities; constructing and training a congestion propagation model, predicting a future congestion path in combination with priority distribution, and identifying bottleneck nodes; and according to a prediction result, cooperatively implementing a grooming strategy from four dimensions of personnel, channels, equipment and processes.

The invention relates to the technical field of wireless communication, and discloses a communication equipment command and dispatch system based on artificial intelligence, the system maps the state of communication equipment into a standardized carrier phase disturbance mode, and after the disturbance mode is adaptively adjusted in combination with channel noise perception and conflict arbitration logic, the communication equipment is commanded and dispatched. Controlling the radio frequency front end to carry out injection; the adjacent communication equipment directly detects the disturbance mode to trigger the cooperative scheduling action by monitoring the physical error signal of the carrier synchronization loop of the adjacent communication equipment, and the implicit cooperative mode based on the physical layer beacon is constructed, so that the complex high-level signaling interaction required by the traditional scheduling is avoided, and the scheduling efficiency is improved. According to the method, the millisecond level, determined by a software protocol, of the collaborative response time between the devices is substituted into the microsecond level determined by the hardware physical layer response entscheid, the system can still maintain reliable transmission of scheduling instructions when facing high-density concurrent conflicts and strong channel noise, and a technical implementation path is provided for ultra-low-delay and high-reliability communication.

The invention provides a node task migration and scheduling system based on digital twinning, and relates to the technical field of computer system structures and data processing. Computing resource interference in a multi-tenant sharing environment is quantized by sensing a cross-tenant noise coefficient and a state synchronization complexity entropy in a node micro-architecture; extracting track features of the mobile terminal, calculating spatial discrete variance, generating a self-adaptive migration decision hysteresis factor, and converting the migration decision hysteresis factor into decision damping to inhibit invalid high-frequency reciprocating migration; constructing a digital twin sandbox before physical cutover, cooperating with a chaos scene injection engine to inject a composite fault operator into a bottom layer, and performing actuarial calculation on service continuity retention after risk adjustment by using a fidelity integrator; and finally, a bottom layer controller is linked through safety baseline comparison to execute physical flow switching. According to the method, network boundary deduction is completed on the premise that physical bandwidth is not consumed, the interruption risk caused by state hard switching is avoided, and smooth transition of stateful services is effectively guaranteed.

The invention discloses an elastic resource beforehand early warning and scheduling method and system based on load prediction, and belongs to the technical field of cloud computing resource management and scheduling, and the method comprises the following steps: constructing a historical data collector, and collecting and preprocessing multi-dimensional time sequence load data of a business system in real time; constructing a prediction analysis engine, training a prediction model, accurately predicting future busy and idle time points and period trends of each service system, and generating a quantitative load prediction report; an elastic planner is constructed, and an elastic resource pre-expansion plan is automatically generated and executed before a business peak arrives based on a load prediction report and a preset strategy rule; constructing an intelligent scheduler, and executing an elastic capacity expansion action according to the elastic resource pre-capacity expansion plan; and outputting a complete elastic resource beforehand early warning and scheduling system based on load prediction. According to the invention, the response speed and service quality of the system are remarkably improved, and the method is suitable for resource management requirements in complex environments such as multi-cloud and mixed-cloud environments.