446 results about "Scheduling system" patented technology

The application discloses a logistics distribution path optimization and dynamic scheduling method and system based on artificial intelligence, relates to the technical field of logistics distribution, and comprises the following steps: collecting real-time data, processing the collected data, and obtaining a data set for path optimization and scheduling; constructing a logistics distribution path optimization model, calculating an optimal distribution path; and dynamically scheduling distribution vehicles and adjusting in real time according to real-time traffic and demand changes. The application adopts real-time feedback data to realize dynamic path optimization, continuously collects traffic, weather, vehicle state and cargo temperature and humidity data in the distribution process, and adjusts the path in real time. Various factors are comprehensively considered to improve the accuracy of path scoring and the distribution efficiency. Through calculation of the urgency of an order and the characteristics of goods, the dynamic scheduling system of the application can intelligently adjust the priority of the order, and is suitable for various environmental conditions.

Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which automatically manages appointment scheduling. The analytic server receives a customer request to schedule an appointment. The analytic server determines the required data from both customer and service provider for making the appointment. The analytic server retrieves customer data comprising requested service attributes, user preferences, users attributes from internal database and external data source. The analytic server retrieves service providers' data comprising provider service attributes, providers' attributes from internal database and external data sources. The analytic server accesses external data source by web crawling various websites. The analytic server executes an artificial intelligence model to predict user preferences and needs. The analytic server determines potential service providers best matching the customer's input or predicted preferences. The analytic server generates an appointment for each matching service provider and transmits an electronic message comprising the appointments to customer device.

This application provides a scheduling method, which includes: a scheduling system receiving a resource request or a service request; wherein the resource request or service request includes first resource information or first service information and demand information; wherein the demand information includes at least performance demand information and energy consumption demand information; responding to the resource request or service request, and determining a target node based on the first resource information or first service information, demand information, and node information; wherein the node information includes node performance information and node energy consumption information. This application differs from the traditional nearest-neighbor scheduling method by comprehensively considering the node's performance information and energy consumption information, thereby scheduling service requests to the optimal target node and improving the computational efficiency of network resources.

The application discloses a steel bar production line intelligent scheduling system based on artificial intelligence. In the application, the dynamic adaptation problem of multi-specification steel bar mixed production is solved through a virtual-real fusion decision mechanism. In view of the demand of alternate rolling of screw thread steel and bright round steel, the system analyzes order parameters in real time, dynamically optimizes the matching logic of roll gap adjustment sequence and cold bed stepping rhythm, and ensures that the steel bars with different surface characteristics reduce transition waste during process switching. Through equipment health degree prediction and material mechanics simulation, the peak value of the main drive of the rolling mill is automatically reduced and the cooling time is prolonged when high-strength steel bars are rolled, so that the roll peeling or internal cracks of the steel bars caused by stress mutation are avoided. For small batch orders, the system intelligently compresses the shutdown window of die and groove changing, pre-adjusts the process parameters by using the idle gap of the equipment, and significantly reduces the production capacity breakpoint caused by specification switching.

The application relates to the technical field of task scheduling, in particular to a task scheduling method and a task scheduling system, which comprises the following steps: determining a working mode based on the sensing information of a cooperative sensing module and each scheduling center associated with the cooperative sensing module; if it is determined that the working mode of the current scheduling center is in a multi-live mode, obtaining a target to-be-processed task and a corresponding configured execution object from a to-be-processed task table; wherein all scheduling centers corresponding to all machine rooms are alive in the multi-live mode; the execution object is a scheduling center for executing the corresponding to-be-processed task; if it is determined that the execution object is the current scheduling center, controlling the current scheduling center to execute the target to-be-processed task; otherwise, determining a target execution object from between the current scheduling center and the execution object according to the execution device of the execution object, and executing the target to-be-processed task. Therefore, the application can simply, efficiently and safely realize task scheduling.

The present application discloses a method for implementing a mobile robot having an intelligent body behavior. First, a feature database of a workpiece to be processed is established manually or by a robot, and then a workshop digital management system sends a task instruction comprising task data, a target machine tool, a device number, and a processing time to the robot by means of a group control scheduling system. Then, on the basis of the instruction, the robot autonomously identifies the workpiece to be processed. When identification fails, the robot continues to scan a next workpiece to be processed, and when the identification succeeds, the robot autonomously plans a route and transfers the workpiece to be processed to the target machine tool for processing. According to the present application, the robot is endowed with self-determination and self-behavior generation capabilities under particular constraints, so that efficient and autonomous operation tasks can be implemented.

The application relates to the technical field of data governance, in particular to a task management method and device and computer equipment. The method is applied to a task scheduling system and comprises the following steps: determining a current to-be-executed subtask from a task list, determining a target agent corresponding to the to-be-executed subtask according to a type label of the to-be-executed subtask; calling the target agent to execute the to-be-executed subtask, obtaining execution result data, and feeding back the execution result data to a main agent; when it is determined that the execution result data represents execution failure, calling the main agent to process the execution result data and a memory chain of the main agent, obtaining an adjusted task sequence; updating the task list based on the adjusted task sequence, and continuing to execute each subtask that has not been executed in the updated task list. The scheme can improve the execution efficiency of the task scheduling system.

The application discloses an intelligent warehouse scheduling method and device, relates to the technical field of intelligent warehouse logistics, and is applied to a scheduling system and comprises the following steps: acquiring target goods, determining the actual weight of the target goods through the vehicle-mounted controller of an automatic guided vehicle, the current temperature of the goods and the lifting height; acquiring the actual weight and the target goods ID, screening the goods storage positions according to the size relationship between the actual weight and a target threshold value, and obtaining a candidate storage position set; determining the target score of the candidate storage positions based on the deviation change quantity and the balance change quantity of each storage position after the target goods are placed in the candidate storage positions, and determining the target storage position based on the target score; sending a carrying instruction to the automatic guided vehicle based on the target storage position and the target goods ID, so as to carry the target goods to the target storage position based on the carrying instruction; and determining the position and weight record information of the target goods, so that the automatic guided vehicle acquires the goods based on a taking instruction and completes the warehousing. The safety and stability of the storage system are ensured.

This invention relates to the field of distributed computing technology, and in particular to a unified registration and scheduling system for AI agent capabilities in edge-cloud collaboration, comprising: a dynamic behavior mapping module for capturing client interaction behavior features and mapping them to behavior control topology, performing path adjustment when deviations occur, and registering the control behavior to a unified MCP capability center; a semantic feature compression module for dimensionality reduction and compression of the original multimodal data stream to generate low-dimensional semantic feature stubs; an asynchronous state alignment module for converting feature stubs into asynchronous stateless message streams and reconstructing context mirror data on the cloud side; an inference stream scheduling module for generating inference stream scheduling strategies based on multi-dimensional states; a thought chain flow control module for dynamically truncating the streaming output of the large model according to the strategy and extracting core control segments and confidence levels; and a residual collaborative execution module for sending the core control segments to the edge for residual logic completion, driving the topology to execute control behaviors.

This invention discloses an edge-cloud collaborative computing and dynamic scheduling system and method for omnidirectional robots, relating to the field of edge-cloud collaborative technology. The system includes: during industrial operation of the omnidirectional robot, acquiring torque, pose, and control commands at the edge and aligning them in time to form a sequence of key operational states; generating predictive features through sliding time window differential and time-weighted aggregation; comparing the predicted features with historical normal features to obtain predicted trigger values; initiating high-frequency uploading of key data when the trigger value exceeds the local risk reference band of the corresponding operational stage; the local risk reference band is constructed in stages by historical similar devices; edge nodes cache data according to operational stages and issue upload and retention strategies to achieve adaptive data scheduling under edge-cloud collaboration. This solves the problems of delayed risk information transmission and difficulty in timely capturing the early risk evolution process of industrial operations caused by existing abnormal trigger-based uploads.

This invention discloses a power system for load forecasting and dynamic scheduling, relating to the field of power system technology. The system includes: constructing an initial simulation environment for the corresponding scheduling cycle in a digital twin model based on grid status and multi-dimensional load forecasting information from power grid, grid, load, and storage; generating a baseline scheduling plan by solving the solution; selecting candidate scheduling strategies with the highest comprehensive evaluation and associating them with grid status and multi-dimensional load forecasting information from power grid, grid, load, and storage, storing them together in a dynamic strategy library; executing the candidate scheduling strategies and collecting actual operating data to calculate the strategy execution deviation; and adaptively correcting the parameters of the digital twin model and the generation rules of the counterfactual scheduling strategy based on the strategy execution deviation. This invention continuously corrects the parameters of the digital twin model based on actual operating data and adaptively adjusts the disturbance strategy, resulting in higher prediction accuracy and simultaneous improvement in economic efficiency and safety margin of the scheduling strategy.

This application relates to an SMT (Surface Mount Technology) material delivery scheduling method and system. It includes: executing a material delivery task; when reading the identification code of a material tray fails, identifying the failure type as optical overexposure, and reporting the failure information to a central scheduling system; the central scheduling system updates the execution history information of the material delivery task based on the failure information; when the central scheduling system needs to allocate an automated guided vehicle (AGV) to the material delivery task, it queries the execution history information of the material delivery task; if the query results indicate that the material delivery task has failed, it identifies the type of AGV that caused the failure in the execution history information, excludes the AGV type that caused the failure from the available AGVs, and selects AGVs with different physical configurations for allocation. This solves the problem of code reading failure caused by optical overexposure of high-gloss material trays in the SMT production environment.

The application discloses a vertical and plane transportation mixed scheduling system and method based on space-time coordination, relates to the technical field of intelligent scheduling, and comprises the following steps: obtaining new transportation equipment information, determining initial task allocation weights of the transportation equipment based on rated operation capacity of the transportation equipment; constructing a historical coordination efficiency set and a real-time coordination efficiency set; constructing a historical load characteristic curve; obtaining a current task allocation request quantity, matching a composite load value of the same request quantity in the historical load characteristic curve, calculating an average value as a standard operation load and determining a fluctuation range; combining the total number of the same type of equipment before and after expansion to calculate a theoretical load value; calculating a current actual load value; comparing the actual load value with the theoretical load value, and adjusting the task allocation weights when the actual load value exceeds the fluctuation range; and counting the total number of paired tasks of the vertical and plane equipment and the space-time coordination hit number, calculating a hit rate, and allocating the task weights based on the hit rate, so that the vertical and plane transportation mixed scheduling is realized.

The application provides a flexible scheduling group and a scheduling method. The scheduling group comprises a manufacturing execution system, a logistics execution system, an intelligent group control system, a logistics intelligent trolley scheduling system and a process intelligent trolley scheduling system. The intelligent group control system receives a production plan issued by the manufacturing execution system, receives inventory material information of the logistics execution system, receives vehicle position information of the logistics intelligent trolley scheduling system and the process intelligent trolley scheduling system respectively, and formulates scheduling tasks of the process intelligent trolley and the logistics intelligent trolley in real time. Through deep cooperation of the intelligent group control system with the manufacturing execution system, the logistics execution system and other systems, overall management of the whole production process is realized. Through real-time sensing of data of environmental changes and / or data of demand fluctuations by the intelligent group control system, and through on-demand pulling, the coordination and stability of line operation are improved through cooperation with multiple systems.

An adaptive redundancy-scheduling system for column-oriented neural-network compute arrays is disclosed. The system periodically verifies correctness of compute columns through concurrent redundant-column execution and adjusts test intervals and substitution thresholds in response to observed error statistics. Redundant columns are activated only when measured fault probability exceeds a programmable confidence level, maintaining yield and reliability with minimal performance impact. Verification tasks occur during non-critical compute intervals, and error history is retained for long-term reliability prediction.

The application belongs to the technical field of production scheduling system, and particularly relates to a rubber belt production line production scheduling system based on intelligent optimization, which comprises a sensing layer, equipment operation data are monitored through sensing equipment, equipment data are sorted into equipment data packets and uploaded to a control center, the equipment data packets comprise production states, operation states, process parameters and material inventories, static specifications, dynamic performance and plan constraints of the equipment are converted into mathematical constraints recognizable by a scheduling algorithm, equipment processing boundaries are defined through an adaptive matrix, small-specification orders are prevented from being allocated to large equipment or high-precision requirements from being allocated to low-precision equipment, equipment specification mismatch rates are reduced, and rework rates caused by insufficient equipment capacity are reduced, and furthermore, production capacity expectations are dynamically corrected through a decay curve, schedule delays caused by scheduling based on theoretical values are avoided, schedule progress deviations are reduced by production capacity decay correction, and order on-time delivery rates are improved.

This invention relates to the fields of artificial intelligence, multi-agent collaborative scheduling, and federated security management. Specifically, it is a federated capability management and unified scheduling system for multi-agent collaboration, comprising: a central scheduler receiving collaborative task requests across multiple local agent nodes and obtaining capability declaration information containing only the capability tags of the local agent nodes; constructing prompt word templates and generating an initial fine-grained task decomposition scheme; extracting cross-domain call links, statistically analyzing the frequency of cross-domain calls and data interaction dimensions, predicting the communication interruption probability of local agent nodes and comparing it with a security risk threshold; if the probability is lower than the threshold, a fine-grained scheme is adopted; if the probability is higher than or equal to the threshold, a minimum-privilege interaction coarse-grained scheduling scheme is generated and distributed; and local agent nodes executing sub-tasks and reporting their status to maintain core business continuity without exposing underlying private data.

The application relates to an automatic vertical warehouse storage system and a storage method thereof, the automatic vertical warehouse storage system comprising a storage workshop and a storage management control system covering the storage workshop, the storage workshop being internally provided with an automatic vertical warehouse, a first-layer storage area, a second-layer storage area and a material transportation system, the storage management control system comprising a storage management system WMS and a storage control system WCS, the storage management system WMS comprising a material management system SAP docking, and the storage control system WCS comprising an AGV scheduling system; the automatic vertical warehouse storage system is reasonable in the division of the positions of various areas, and the whole process of a material unloading area, a unpacking and replacing area, a material inspection area, a material to-be-inspected area, a warehousing area, an outbound sorting area and a material batching buffer area is connected through AGV, the whole transfer line flexible connection is realized, the continuity and accuracy of an operation process are improved, automatic material carrying and transportation are realized, the production efficiency is improved, and the labor cost is reduced.

This invention discloses a production data-driven intelligent scheduling method and system for mechanical equipment, relating to the technical field of equipment scheduling. The method includes: constructing a set of equipment operating characteristics; comprehensively evaluating the current load rate, processing capacity, and failure risk of each piece of mechanical equipment; constructing a scheduling objective function using equipment survivability scores as scheduling decision weight parameters; performing task allocation and processing path optimization calculations to generate an intelligent scheduling scheme for the mechanical equipment; and monitoring the scheduling execution process in real time to adaptively adjust the production scheduling scheme. This invention solves the technical problems in existing technologies where static scheduling cannot adapt to real-time changes in equipment status and lacks a dynamic risk feedback mechanism, leading to equipment overload, unplanned downtime, and cascading production delays during scheduling scheme execution. It achieves the technical effects of improving the production scheduling system's adaptability to dynamic equipment operating conditions, reducing the risk of unplanned downtime, and optimizing equipment utilization and production delivery reliability.

This invention discloses a satellite communication terminal simulation task orchestration system and device based on intelligent scheduling. It adopts a microservice architecture, with five modules working collaboratively via RESTful APIs or gRPC. The system constructs a multi-dimensional global resource view, combining historical data to drive a regression prediction model to quantify resource requirements. The intelligent scheduling decision module models complex task allocation as a multi-dimensional optimization problem, using the PSO algorithm combined with a multi-objective fitness function to find the optimal solution, supplemented by a task preprocessing mechanism to improve efficiency and effectiveness. The system manages task dependencies using a DAG and deeply integrates with Kubernetes to achieve automated task orchestration, status monitoring, and fault recovery. This effectively solves the pain points of traditional scheduling, such as resource fragmentation and static configuration waste, achieving dual-dimensional global dynamic scheduling of business and resources. This significantly improves cluster resource utilization and simulation execution efficiency, adapting to the needs of large-scale, high-concurrency simulation scenarios.

The application relates to the field of construction scheduling control, in particular to a water conservancy engineering construction mechanical group collaborative operation intelligent scheduling system, which comprises the following steps: a geological mechanical coupling twin platform constructs a bidirectional coupling closed loop of a geological field and construction mechanical behavior according to real-time working condition data of a region to be constructed, obtains a dynamic geological attribute field quantity, and fits a geological disturbance sensitive coefficient of the region to be constructed; a cloud edge end collaborative network collects real-time working condition data through an intelligent agent node; a cloud computing center solves a global construction task scheduling scheme by adopting a multi-objective optimization algorithm; an edge computing node adopts a multi-agent deep reinforcement learning algorithm to perform real-time rolling optimization on the collaborative operation of construction machines in a local jurisdiction region according to a screened optimal local construction task distribution scheme, generates a local collaborative scheduling instruction, and schedules each construction machine. The scheduling system can realize real-time sensing of geological changes, reduce the probability of engineering accidents, and dynamically optimize the collaborative operation of the mechanical group.

The present application relates to a kind of car body production sequence scheduling system and scheduling method, so that car body is after welding process, enter assembly process before, in coating process, car body production sequence adjustment is carried out, the intelligent scheduling of car body production sequence between different processes is realized, and car body of production plan overtime can be timely discharged;The order of discharge queue is adjusted by vertical warehouse scheduling system, can meet the demand of adjusting spraying order according to color in coating process without affecting assembly plan order, and the discharge queue in production can make the same color car body sort together, reduce the color switching frequency in coating spraying process, improve production efficiency;Pre-paint grouping stereoscopic warehouse is set in coating process, between pretreatment electrophoresis line and topcoat line, is sorted and adjusted by discharge queue in coating process, can reduce the situation that assembly plan is adjusted due to car body needing to be repaired from assembly process point because of coating quality defect, improve production efficiency.

The application belongs to the technical field of video stream real-time analysis, and particularly relates to a high-concurrency video stream real-time analysis engine scheduling system and method, which comprises a scheduling management layer, a task execution layer and a resource management layer; the scheduling management layer comprises a multi-dimensional task evaluation module, an adaptive scheduling decision module, a multi-level priority queue management module, a scene-aware scheduling module and a dynamic load balancing module; the task execution layer comprises a video stream receiving module, a task decomposition module, a parallel execution engine and a result aggregation module; and the resource management layer comprises a resource monitoring module, a resource allocation module and a resource recycling module. Through the multi-dimensional task evaluation mechanism, the adaptive resource scheduling strategy, the multi-level priority queue, the scene-aware scheduling and the dynamic load balancing, the application realizes low resource consumption, high-concurrency processing and real-time response of video stream analysis scheduling, and significantly improves the video stream real-time analysis performance and resource utilization efficiency in the multi-camera, multi-task and multi-scene conditions.

This invention belongs to the technical field of server resource scheduling and management, and relates to a cascaded scheduling system for server AI computing power modules for elastic computing. This system utilizes a situational awareness unit deployment module to collect micro-level operational data from the modules and generate raw sampling information; a real-time situational vector generation module constructs a multi-dimensional real-time situational vector; a global pressure map construction module maps the vector to generate a global cascaded pressure map for the pipeline; a dynamic bottleneck diagnosis module identifies abnormal nodes and determines hard bottlenecks and pseudo-blockages; a collaborative flow control strategy orchestration module generates push-pull linkage control commands; a predictive performance state adjustment module generates power level adjustment commands based on prediction time; and a command issuance and feedback closed-loop module drives execution and corrects parameters. This invention solves the problems of difficult accurate diagnosis of micro-level performance bottlenecks and the lack of global collaborative dynamic adjustment capabilities for data flow and power consumption states in heterogeneous AI computing power module cascaded systems.

This invention relates to computing power scheduling technology and discloses an intelligent computing power scheduling method and system based on dynamic programming, comprising the following steps: S1: Real-time monitoring of the current resource status of computing power nodes; S2: Based on the current resource status and combined with a preset time-series historical window, extracting historical resource dynamic features of each computing power node; S3: Inputting the historical resource dynamic features into a pre-trained time-series prediction model to predict the resource performance trend of computing power nodes over a future period; S4: Constructing a dynamic programming state space for different time nodes corresponding to different computing power nodes; S5: Defining and solving an objective function based on the above dynamic programming state space; S6: Generating an optimal computing power scheduling strategy based on the objective function solution, dynamically adjusting computing power scheduling. This achieves rapid computing power scheduling under dynamic programming, while introducing a spatiotemporally aware cost function and an incremental learning closed loop, enhancing the adaptive learning of the scheduling system.

The application relates to the field of unmanned aerial vehicle inspection, and provides an intelligent scheduling system for unmanned aerial vehicle inspection of power collection lines of a new energy station, a three-dimensional dynamic wind field construction module, an energy consumption dynamic evaluation module, a three-dimensional flight path planning module, an edge side defect identification module and a cluster dynamic scheduling module. The three-dimensional dynamic wind field construction module is used for constructing a three-dimensional dynamic wind field; the energy consumption dynamic evaluation module is used for dynamically evaluating total expected energy consumption; the three-dimensional flight path planning module is used for generating a three-dimensional obstacle avoidance flight path; the edge side defect identification module is used for identifying power collection line defects in real time; and the cluster dynamic scheduling module is used for monitoring in real time and triggering rescheduling. When the unmanned aerial vehicle is out of energy or hovers for a long time, the system can calculate residual tasks of the corresponding unmanned aerial vehicle, obtain an optimal relay distribution sequence based on the residual tasks, confirm a relay execution machine through the optimal relay distribution sequence, and millisecond-level schedule the relay execution machine to replace the residual tasks of the original unmanned aerial vehicle. Meanwhile, the system can also update the cluster scheduling state in a timely manner, so that the inspection process is ensured to have no breakpoints, no gaps and no information loss.

The application provides a new energy electrical equipment scheduling method and system based on a smart grid, relates to the fields of smart grids and new energy power scheduling, and acquires operation state information and scheduling instructions of new energy electrical equipment, evaluates operation pressure of the new energy electrical equipment when the scheduling instructions are executed according to the information, analyzes long-term operation conditions of core components, compares actual output and predicted output of the new energy electrical equipment, identifies a deviation mode with directionality and accumulation in the predicted output, dynamically adjusts a scheduling strategy of the new energy electrical equipment, and optimizes the operation state of the new energy electrical equipment and maintains the stability of the power grid. The method effectively solves the problems that the existing scheduling system is difficult to identify and adapt to the change, the prediction deviation is accumulated, the scheduling instruction deviates, the new energy equipment is operated in a non-optimal efficiency interval for a long time, hidden losses are caused, and the stability of the power grid frequency and voltage is finally affected.

The application relates to a job scheduling system, equipment and medium for strong and weak ability mixed nodes, the system comprising: a coordinator module configured to maintain a cluster topology structure and execute a consistency election protocol; a scheduler module configured to generate a task dispatch strategy according to a global resource pool state and a job dependency relationship; an executor module configured to receive and execute a job instruction issued by the scheduler module; a trigger module configured to listen to an external event source and convert an event meeting a preset condition into a job scheduling request and send the job scheduling request to the scheduler module; and a communication abstraction layer configured to shield transmission differences of a bottom layer from an upper layer application according to attributes of a target node and a uniform resource identifier prefix. In the above embodiment of the application, the election protocol integrated with the node capability mask field completely solves the problem that a weak node is mistakenly selected as a master node, leading to instability of a cluster. The application effectively adapts to a narrowband and high-delay network environment and improves communication reliability in a weak network.

This invention relates to the field of weight allocation technology, specifically to a distributed dynamic weight allocation method and system based on real-time deviation correction, comprising the following steps: acquiring task execution and performance deviation data, adjusting node resource weight ratios, and generating dynamic allocation results. In this invention, a dual deviation calculation mechanism based on task execution time and performance output is constructed to achieve rapid response to changes in node operating status. By extracting weight adjustment objects and setting differentiated weight adjustment ratios through a normalized offset amplitude sequence, the invention effectively overcomes the dependence of traditional models on static parameters and lag indicators. Through periodic refined calculation and dynamic ratio measurement, the node resource allocation ratio is corrected in real time, enhancing the scheduling system's ability to locate nodes with abnormal performance, improving the rationality and timeliness of resource allocation, optimizing the load balancing effect between nodes, and enhancing the adaptive control capability against nonlinear changes in a multi-task environment.