1422 results about "Scheduling system" patented technology

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 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 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 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 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 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 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 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 a heterogeneous computing power cooperative scheduling system and method for mixed precision training, and belongs to the technical field of artificial intelligence computing. The system comprises a computational graph analysis and operator portrait module which is used for analyzing and dividing a model computational graph and extracting operator features; the heterogeneous hardware capability sensing and matching module is used for managing performance files and real-time states of heterogeneous hardware in the cluster and matching optimal execution hardware for each calculation partition; and the data flow coordination and pipeline parallel controller is used for generating a global execution plan, managing cross-device data dependence and communication and calculating overlapping optimization execution efficiency through communication. According to the method, the problem of low scheduling efficiency of mixed precision training in a heterogeneous environment is solved, automatic and accurate mapping from a calculation task to heterogeneous hardware is realized, the training speed is remarkably improved, the training cost is reduced, and the overall resource utilization rate of a cluster is improved.

The invention provides a rounded-corner container transportation path dynamic optimization scheduling method and system, and the method comprises the steps: quantifying three constraints of the size, the gravity center and the loading and unloading priority of a rounded-corner container, converting the three constraints into a matching formula, a path constraint threshold value and a weight rule, and constructing a weighted multi-objective optimization function in combination with the transportation cost, the time and the cargo damage risk; a genetic algorithm and ant colony algorithm mixed framework is built, a constraint adaptation layer is embedded to filter invalid solutions, and the iteration efficiency is improved; two types of algorithm operators are improved, and a constraint satisfaction degree, a loading and unloading priority and a dynamic parameter adjustment mechanism are fused; dividing multiple regions into sub-region optimization by adopting a divide-and-conquer strategy, and adapting to a large-scale dynamic scene through cross-region collaboration and local re-optimization; a full-dimension verification scheduling scheme in iteration is carried out, algorithm parameters are automatically adjusted based on constraint violation information, and iteration is terminated or constraint relaxation is started according to preset conditions; an improved algorithm is integrated to a dynamic scheduling system, real-time data are connected, parameters are optimized through a self-learning module, and a manual intervention interface is reserved.

The invention provides an intelligent customer service interaction content recommendation method and system based on artificial intelligence, and the method comprises the steps: obtaining power grid equipment state data and user behavior data, and carrying out the weighted fusion through an attention mechanism, and outputting a fusion feature vector; predicting a prediction vector set of a future time period based on the fused feature vector; screening a demand event set higher than a threshold in the prediction vector set, and generating a recommended content set; performing emotion recognition according to a current input text of the user, and calculating a recommended content score and a pushing priority score in combination with the recommended content set; a cross-modal consistency regular term is introduced to correct the push strategy vector, and an adjusted recommendation push score and a corresponding recommendation content set are obtained; and converting the adjusted recommendation push score and the corresponding recommendation content set into a scheduling task, and performing linkage execution with a scheduling system. According to the invention, efficient, active and intelligent upgrading of the intelligent customer service system in a power grid scene is realized.

The invention relates to the technical field of power system automation, and particularly discloses a cross-domain heterogeneous computing power real-time calling and unified scheduling system for the power industry, which comprises a computing power resource global sensing unit, a business demand dynamic modeling unit, a cross-domain unified scheduling decision unit and a task execution and feedback control unit, according to the method, heterogeneous computing power states are collected in real time, task requirements are dynamically modeled, a scheduling scheme is generated based on multi-objective optimization, and task execution monitoring and load balancing are realized by means of closed-loop feedback, so that the utilization efficiency of global computing power resources is improved, and the real-time performance and reliability of power business are guaranteed.

The invention discloses a heterogeneous computing power resource dynamic scheduling system and method based on multi-objective optimization, and the system is deployed in a digital ecological platform complex based on multivariate consensus and embedded intelligent management. Comprising a platform access module, a data acquisition and perception module, a multi-target modeling and optimization module, a dynamic scheduling and execution module and a fault-tolerant mechanism module. The system registers as a computing power scheduling service node through an intelligent contract interface, obtains and verifies the compliance of a computing task, collects heterogeneous computing power resource node state data, and generates a scheduling decision by dynamically adjusting the weight of a target function; evaluating task suitability by using an AMCU suitability scoring model, and executing a scheduling AMCU strategy; according to the method, the technical problems of low resource scheduling efficiency and poor fault-tolerant capability in a heterogeneous computing power environment are solved, and the system throughput and the resource utilization rate are improved.

The invention discloses an inference task collaborative scheduling system and method oriented to multiple terminals of a swan gap. The inference task collaborative scheduling system comprises a communication module Broker, a system monitoring module SystemProfilter, a scheduling module Scheduler and an inference task execution module Worker. According to the method, a structured resource state vector is constructed based on an NDK native interface so as to comprehensively represent the real-time availability of equipment; a lightweight MQTT protocol is adopted to support low-overhead and high-robustness cross-terminal state synchronization and task distribution; a comprehensive load scoring mechanism fusing task priorities and dynamic weights is designed, a multi-objective optimization problem based on a non-dominated sorting genetic algorithm (NSGA-II) is introduced, task delay is minimized in a combined mode, terminal loads are balanced, and energy consumption is controlled; meanwhile, the execution time delay is efficiently estimated in combination with a proxy model based on linear regression, and the scheduling overhead caused by real reasoning and calling is avoided; the whole architecture is deeply adaptive to a swan-mong system, and is compatible with an Android platform through modular packaging and unified communication interface design, and efficient, self-adaptive and cross-platform collaborative scheduling oriented to a swan-mong multi-terminal reasoning task is realized for the first time.

The invention discloses a computing power resource dynamic scheduling system integrating environmental perception and power self-balancing, and relates to the technical field of computing power resource scheduling. According to the system, multi-dimensional data inside and outside a specified computing power facility operated by a server cluster are collected in real time through a data collection and environment perception fusion module, and dynamic / static fusion processing is carried out on the multi-dimensional data and current environment perception parameters, so that comprehensive perception of the environment and the operation state is realized; then, resource configuration is optimized by combining a combined scheduling and self-power balance control module with a fusion processing result; finally, a hierarchical scheduling scheme is generated through a hierarchical scheduling and multi-scene adaptation verification module, and whether the generated hierarchical scheduling scheme adapts to the current scene or not is judged by combining progressive derating judgment and error-tolerant rate verification, so that dynamic linkage regulation and control of the load, the environment and the power are realized, the problem of unbalanced power distribution in computing power scheduling is effectively solved, and the service life of the computing power scheduling system is prolonged. And the energy efficiency ratio and the environment adaptive capability of the server cluster are improved.

The invention discloses an automatic driving carrying equipment scheduling system and method for an industrial robot. The system comprises a plurality of automatic driving carrying devices, a central dispatching device and corresponding communication modules. The system adopts a multi-agent reinforcement learning framework and combines a graph neural network processing environment topological structure to realize dynamic path planning and multi-device collaborative scheduling; integrating an energy consumption prediction mechanism based on a Kalman filter, and bringing energy consumption factors into a task allocation decision; meanwhile, a fault-tolerant management mechanism based on a distributed account book and federated learning is established, and fault detection and rapid recovery are achieved. The technical modules are deeply coupled, and a unified collaborative optimization framework is formed through reward function design, utility function optimization and fault probability calculation of multi-agent reinforcement learning. According to the method, the problems of poor dynamic environment adaptability, isolated decision making of each module, extensive energy consumption management and the like in the prior art are effectively solved, and the overall efficiency, energy efficiency and reliability of a scheduling system are remarkably improved.

The invention discloses a supply chain logistics flexible scheduling system based on multi-source data fusion, relates to the technical field of logistics scheduling, and solves the technical problems of extensive order scheduling and path optimization and insufficient dynamic response capability. Comprehensive integration of order, transport capacity, inventory and external environment data is realized, an order emergency degree classification standard is quantified, an improved algorithm and a K-means clustering + genetic algorithm are combined, and an optimal basic scheduling path considering timeliness and cost is generated; meanwhile, collaborative optimization of inventory and transport capacity is achieved based on resource characteristics such as the inventory turnover rate and the transport capacity load rate, the daily average delivery order amount of a single vehicle is increased, a double dynamic adjustment mechanism of timed refreshing and event triggering is adopted, refreshing intervals are set according to different transport scenes in a differentiated mode, and the efficiency is improved. The method can quickly respond to abnormal conditions such as sudden congestion and address deviation, can automatically adjust the path, and can reduce the delay rate caused by congestion.

The invention discloses a multi-agent collaborative logistics distribution and scheduling system based on swarm intelligence emergence optimization and a method thereof, and relates to the technical field of swarm intelligence, multi-agent systems, intelligent logistics and distributed optimization, in particular to a multi-agent collaborative logistics distribution and scheduling system based on swarm intelligence emergence optimization and a method thereof. The system adopts a completely distributed architecture and is composed of a plurality of agents for autonomous decision making, each agent comprises a sensing module, a decision making module, a communication module and an execution module, cooperation is achieved through local sensing and neighborhood communication, and a central controller is not needed. The system performs path optimization and obstacle avoidance by using a coupling mechanism of a pheromone field and a potential field function, and supports multi-scale collaboration, distributed consensus decision and self-organization capability. The method comprises the following steps: acquiring local information by an intelligent agent, planning a path based on a pheromone field and a potential field, and realizing task allocation and conflict resolution through interaction. According to the system, the energy consumption can be effectively reduced by 28%, the efficiency is improved by 5%, and the system performance exceeds the total sum of intelligent agents by 40-50%.

The invention discloses a distributed RID receiving system based on edge multi-source fusion, which relates to the technical field of low-altitude supervision, and is characterized in that a distributed RID receiving node cluster adopts a fixed node and maneuvering node collaborative grid deployment scheme to capture and preliminarily analyze low-altitude aircraft RID signals in real time, and node data is output after the low-altitude aircraft RID signals are processed by a primary fusion algorithm; the edge multi-source fusion processing unit is deployed in each management and control partition, converges node data in the area, completes calibration, de-duplication and noise reduction through a secondary fusion algorithm, and outputs structured RID data; the cloud management and control platform realizes global data overall planning, situation visualization, intelligent decision making and instruction issuing through a three-level fusion algorithm, and is linked with the urban low-altitude scheduling system to form closed-loop management and control; the clock synchronization module provides nanosecond time reference for the whole system. According to the method, the problems of incomplete coverage and insufficient data processing precision of a traditional RID receiving scheme are solved, and global sensing, accurate data processing and closed-loop management and control of the low-altitude aircraft are realized.

The invention discloses a power flow out-of-limit elimination method and device based on section sensitivity and source load characteristics, terminal equipment and a storage medium, and belongs to the technical field of power flow out-of-limit elimination. The method comprises the following steps: acquiring unit output data, bus load data, section sensitivity data and section power flow data of the power dispatching system in a preset full time period; dividing the section into a new energy output section, a load center feed-in section and a network loop section according to section sensitivity data, and respectively constructing a new energy output prediction adjustment optimization model, a bus load prediction adjustment optimization model, a non-new energy output adjustment optimization model and corresponding constraint conditions; and solving to obtain an optimal new energy output predicted value, a bus load predicted value and a non-new energy output value, and performing out-of-limit adjustment. By implementing the method and the device, the problem of low out-of-limit elimination efficiency caused by huge calculated amount when the scheduling system is subjected to overall modeling solution in the prior art can be solved.

The invention discloses an electroencephalogram voice annotation calibration and scheduling system and method for individual differences. The system comprises an electroencephalogram signal collecting and preprocessing module, a voice instruction receiving and recognizing module, a rapid individual calibration module, a dynamic task scheduling module, an electroencephalogram feature online decoding and annotation module and an annotation storage and feedback module. Through cooperative work of the two core modules, namely the rapid individual calibration module and the dynamic task scheduling module, the problems that decoding performance is reduced during cross-subject and cross-task switching caused by individual differences, and multi-task labeling efficiency is low due to task scheduling strategy stiffness are solved accurately. Data persistent storage, model online updating and user interaction feedback are completed through storage, optimization and feedback links, periodic retraining is carried out by utilizing daily successful labeling data through a model continuous learning link, and finally, a zero-threshold individual rapid adaptation and high-efficiency multi-task collaborative labeling target is achieved.

The invention provides a laser cutting production line task automatic allocation control method based on multi-device state coupling analysis, and belongs to the technical field of intelligent manufacturing and industrial automation. The method comprises the following steps: constructing a dynamic closed-loop control process through a central control scheduling system: receiving a task information packet of an MES; constructing an equipment state vector based on a real-time station state, and introducing a dynamic weight factor set to generate a weighted state vector; performing task triggering judgment through a coupling triggering judgment function in combination with the task dependency graph and historical task records; when the conditions are met, a task instruction is issued to the target station; and feeding back the state and updating the historical record after the task is completed. The invention further relates to AGV intelligent scheduling, visual positioning compensation, process parameter dynamic adjustment, predictive conflict detection, weight self-optimization and the like. According to the method, the production line cooperation efficiency is remarkably improved, manual intervention and system delay are reduced, and the method is suitable for an intelligent laser processing scene in which multiple devices run in parallel.

The invention discloses a mixed traffic flow-oriented automatic driving taxi dynamic scheduling system and a collaborative decision-making method, belongs to the field of intelligent traffic systems, and solves the problem that in the coexistence environment of manual driving vehicles and automatic driving taxies, the automatic driving taxies cannot be automatically scheduled. The technical problem of how to efficiently and cooperatively dispatch vehicles, accurately predict demands, optimize energy management and improve the overall operation efficiency of the system is solved. The system comprises a scheduling server which is connected with a road side sensing unit, a vehicle-mounted control unit and a charging station management platform. The scheduling server comprises a traffic flow analysis module; a demand prediction module; a dynamic scheduling module; and an energy collaboration module. The system is mainly used for realizing real-time, dynamic and intelligent scheduling and energy management of the automatic driving taxis in the mixed traffic flow, improving the operation efficiency, relieving the traffic jam and optimizing the charging resource utilization.

The invention discloses a cascade reservoir scheduling method and system based on an artificial bee colony algorithm, and the method comprises the following steps: the system collects multi-source hydrological data in real time through a hydrological perception and preprocessing module, and introduces a large language model to carry out the semantic judgment and anomaly labeling of an abnormal hydrological time sequence; inputting the processed high-quality data into a reservoir model construction module, establishing a cascade reservoir optimal scheduling system, and setting corresponding boundary conditions and operation constraints in combination with reservoir scheduling regulations; the scheduling optimization module receives model input, adopts a variable structure taking a water level as a core to construct an optimization individual, and completes population initialization, disturbance generation and fitness evaluation based on a potential solution guide mechanism in an improved artificial bee colony algorithm; the system transmits the scheduling sequence optimized and output by the scheduling optimization module into an LLM intelligent auxiliary module; and the intelligent text interpretation generated by the LLM and the scheduling optimization solution enter a result evaluation and visualization module together. According to the scheduling method and system, a high-quality and physically feasible scheduling scheme can be output within reasonable calculation time.

The invention discloses an electric vehicle charging station energy storage scheduling method based on a multi-agent system. The method comprises the following steps: obtaining and standardizing operation basic data of a plurality of new energy vehicle charging stations; setting five types of agents, defining observation variables and action space, and constructing a multi-agent system model; constructing a global collaborative scheduling network, and executing strategy evaluation and strategy generation; setting a constraint boundary, and constructing a linear programming scheduling model; constructing a training sample, and performing offline training and updating of a global collaborative scheduling network; solving a local optimal scheduling amount based on the real-time operation basic data, and analyzing to generate a control instruction; and issuing an energy storage control instruction and a computing power unit control instruction, acquiring a cooperative scheduling result to generate a final scheduling result set, and submitting the final scheduling result set to an upper-layer scheduling system. According to the method, a multi-agent collaborative scheduling system is constructed, strategy optimization and linear programming are fused, and efficient, stable and executable global collaborative scheduling of energy storage and computing power tasks of the charging station is achieved.

The invention discloses a multi-core scheduling system and method based on interrupt affinity and a storage medium. The technical problem that data locality guarantee and load balancing are difficult to consider at the same time is solved by constructing a self-adaptive optimization closed loop of perception-decision-execution-feedback, and the method comprises the steps that a routing interruption configuration mechanism provides a basis for dynamic adjustment; an interrupt guide task placement mechanism ensures that an interrupt service program and an associated task are executed in the same CPU core, and the cache hit rate is increased; the execution overhead of an interrupt service program is brought into core load statistics through task context switching and a'task + ISR 'two-dimensional precise load evaluation mechanism triggered by ISR inlet / outlet double nodes, and real load awareness is achieved; a load-driven dynamic interrupt routing mechanism is combined with dual control of a hysteresis threshold and cooling time, so that a ping-pong effect is avoided. According to the method, dynamic load balancing is realized on the premise of keeping data locality, and the real-time response performance and throughput of the multi-core system are remarkably improved.

A warehouse logistics scheduling system based on big data analysis specifically relates to the technical field of logistics scheduling, and comprises an order emergency degree calculation component which is configured to integrate order information, customer data and inventory records, perform standardization processing on multi-source heterogeneous data through a big data analysis technology, calculate order emergency degree indexes, and send the order emergency degree indexes to a server; the equipment scheduling optimization component is configured to output a scheduling execution coefficient of equipment based on the order emergency degree index and the equipment real-time state data, and the system dynamic balance component is configured to collect storage energy consumption data, path congestion data and equipment execution data, calculate a system dynamic adjustment coefficient through a multi-objective optimization algorithm and output the system dynamic adjustment coefficient. According to the warehouse logistics scheduling optimization system and method, through deep fusion of big data analysis, an intelligent closed loop of warehouse logistics scheduling is constructed, and the scheduling targets of high timeliness, low cost and stable operation of the system are finally achieved.

The invention provides an asymmetric segmentation scheduling system and method in a heterogeneous GPU cluster, and the method comprises the steps: S1, checking the features of an incoming request, and grouping the incoming request into different request buckets according to the token length; s2, in a heterogeneous GPU cluster environment, optimizing a large language model reasoning instance by adopting a double-layer strategy; and S3, calculating the matching degree of the request buckets and the big language model reasoning instances, and scheduling different request buckets to the big language model reasoning instance with the highest matching degree according to a calculation result. According to the method provided by the invention, the model layer can be asymmetrically segmented according to the computing power and the video memory capacity of each GPU on the premise of satisfying the model parallelism degree constraint, the dynamic balance of the execution duration between stages is realized, assembly line cavitation bubbles are fundamentally reduced, and the overall throughput rate is improved.

The invention relates to the technical field of intelligent scheduling of water supply resources, in particular to an intelligent scheduling system and method for water supply resources in extreme weather, in which a time sequence coupling analysis unit collects pipe network pressure, turbidity and meteorological early warning data in real time, identifies the peak time period of the turbidity abrupt change rate through dual-channel verification, and sends the peak time period of the turbidity abrupt change rate to a cloud server; the dynamic strategy generation unit divides scenes according to the coefficients, starts staged pressure relief in a high-contact-ratio scene, controls total duration and single-stage pressure drop, integrates real-time compensation of flow velocity, generates a pre-pressure-relief instruction in a low-contact-ratio scene, and generates a pre-pressure-relief instruction according to the pre-pressure-relief instruction, and the dynamic strategy generation unit generates a pre-pressure-relief instruction according to the pre-pressure-relief instruction and the single-stage pressure drop in the high-contact-ratio scene. And the pipe network topology compensation correction triggering time is fused, and the execution intensity of two types of instructions is dynamically allocated in a mixed scene, so that the pipe network pressure and water quality collaborative guarantee in extreme weather is realized, and the safety and stability of a water supply system are maintained.

The embodiment of the invention provides a logistics scheduling system, and belongs to the technical field of logistics. The system comprises a task layer which is used for generating an order task; the feedback layer is used for collecting dynamic resource data related to execution of the order task in a logistics site; the decision-making layer is respectively in communication connection with the task layer and the feedback layer, and the decision-making layer is used for adjusting a path planning strategy according to the dynamic resource data and generating a task execution instruction according to the adjusted path planning strategy; and the execution layer is in communication connection with the decision-making layer, and the execution layer is used for scheduling corresponding transportation equipment to execute the order task according to the task execution instruction. According to the embodiment of the invention, the scheduling flexibility of the transportation equipment can be improved.