28results about How to "Improve scheduling efficiency" patented technology

The application belongs to the field of workshop scheduling, and particularly discloses a semiconductor wafer probe test scheduling method and system based on an improved genetic algorithm, which adopts double-layer process-machine coding, and combines auxiliary resource taboo coding as an individual, effectively converts a complex scheduling problem into a coding form through an innovative coding and decoding strategy, and avoids traversal search on all selectable auxiliary resources; global-local hybrid search is carried out through the improved genetic algorithm, on the basis of machine level exchange with the traditional critical path-key block, search on the auxiliary resource immediately preceding work is increased, and neighborhood exploration is carried out based on a load balancing strategy, so that the search efficiency and solution quality are improved. The application can quickly and efficiently solve the semiconductor wafer probe test scheduling scheme, effectively improve the equipment utilization, speed up the scheduling efficiency, and break through the bottleneck of traditional scheduling.

This invention belongs to the field of port operation and management technology. It provides a port project full-process decision management system based on big data analysis, comprising: verifying and analyzing multiple historical estimated arrival times using a hypothetical moving window; determining the optimal moving window using the moving window averaging method and Euclidean algorithm; obtaining a model correction factor based on historical estimates and actual times; obtaining the estimated arrival time; comparing it with the waiting-to-departure time; if a mismatch is found, removing the original stable connecting vessels; reselecting vessels based on connection matching degree and re-evaluating; until a vessel with a time-dimensional connection match is found within the connecting vessel sequence. By optimizing the moving window and introducing correction factors, the accuracy of arrival time prediction is improved, avoiding delays due to time mismatches and effectively improving port vessel scheduling efficiency. This invention solves the problem of how to accurately predict the arrival time of stable connecting vessels and determine whether it matches their waiting-to-departure time.

The application provides a multi-platform cooperative control system and method based on adaptive cruise, comprising a state monitoring platform, an intelligent ticketing platform, a digital twin platform, a dispatching platform and a response review platform; the state monitoring platform obtains real-time operation data of a target power grid through a distributed sensor network and outputs a state diagnosis result; the intelligent ticketing platform automatically generates an operation ticket based on a power grid knowledge graph; the digital twin platform constructs an adaptive dynamic simulation model using the real-time operation data and the state diagnosis result, and simulates and verifies the operation ticket; the dispatching platform dispatches network orders to power grid equipment according to the operation ticket and the simulation verification result; and the response review platform reviews and verifies the state after the dispatching. Through the construction of a multi-platform cooperative control mechanism and the establishment of an adaptive dynamic simulation model, the simulation accuracy and speed are improved, and the safety of the power grid and the efficiency of power grid dispatching are further improved.

This invention discloses a heterogeneous acceleration system and method for analog modulation recognition of large-scale multi-channel signals, comprising: a CPU host processing end and a GPU device computing end; the CPU host processing end is used for asynchronous pipelined processing of data reading, task scheduling, and result distribution, realizing parallel execution of data reading, GPU computing, and result distribution on the time axis; the GPU device computing end completes the entire process of signal preprocessing and deep learning model inference within the GPU memory after the original signal is transmitted to the video memory via the PCIe bus, and does not exchange data with the CPU host processing end until the recognition result is generated. This invention eliminates data transport and I / O blocking through a three-stage asynchronous pipeline architecture and a closed-loop processing link in the entire video memory, and combines parallel preprocessing operators, inference acceleration engines, multi-stream scheduling mechanisms, and dynamic video memory pools to achieve high throughput and low latency recognition of large-scale multi-channel analog modulation signals.

This invention discloses a data transmission method, apparatus, and system based on hierarchical port domains. The method includes dividing a switch port into several port switching units and switching domains. Each port switching unit includes several ports and first and second buffers. Each switching domain includes several port switching units and a temporary buffer. Each port has a pre-set internal routing number including a domain identifier, a unit selection bit, and a port index number. Data packets received by the receiving port are parsed to obtain the target port address and converted into a target internal routing number. If the unit selection bit of the target internal routing number is the same as that of the source internal routing number, the data packet is transmitted to the first buffer for reception by the target port. If the unit selection bits are different, the data packet is transmitted to the second buffer, and it is determined whether the domain identifiers are the same. If they are the same, the data packet is stored in the temporary buffer; if they are different, the data packet is placed in the out-of-band transmission queue for transmission to the target port. This invention offers high data transmission efficiency and good port scalability.

The application relates to a service processing method, system, device and medium based on a dual real-time kernel. A real-time Linux kernel and a real-time virtual machine monitor kernel are constructed, and a message sharing channel is established between the real-time Linux kernel and the real-time virtual machine monitor kernel; according to a preset multidimensional task scheduling strategy and a preset resource allocation and isolation strategy, the real-time Linux kernel is responsible for system service process scheduling and resource allocation at an operating system level, and the real-time virtual machine monitor kernel is responsible for scheduling, device simulation and resource allocation of a virtual machine service. Through the dual real-time kernel, task scheduling is separated, and the efficiency of task scheduling can be effectively improved. Through the optimized multidimensional task scheduling strategy and the resource allocation and isolation strategy, task scheduling is controlled, and the scheduling delay and jitter of real-time tasks in system services and / or virtual machine services are improved in combination with hardware optimization. The system device and the medium based on the method provided by the application can also achieve the above effects.

The application provides an AI supervision system, device and method for high security requirements of a restricted network environment, and belongs to the technical field of cross-over of artificial intelligence auditing, edge computing and trusted security system.The method comprises the following steps: constructing an offline intelligent agent resource cabin in a reinforced carrier, pre-packaging auditing field knowledge, rules and tools into a local encrypted database, generating a structured knowledge container and a lightweight associated network; synchronously deploying an edge rule engine and an embedded tool set to form a basic resource layer; and through the construction of the offline intelligent agent resource cabin and the scene-based intelligent engine, the overall breakthrough of AI auditing in the restricted network environment is realized.

The application discloses a task offloading method for improving task execution efficiency and edge node resource utilization, and relates to the technical field of mobile communication, comprising the following steps: constructing a task offloading model under an edge cooperation environment, and acquiring total task execution time; taking minimizing the total task execution time as a target, jointly optimizing resource allocation decisions, user and edge server association decisions, task offloading proportions, and execution location decisions according to an improved teaching optimization algorithm, and acquiring the minimum total task execution time. The application jointly optimizes task offloading location decisions, task offloading proportions, computing resource allocation, and user and server association strategies, comprehensively optimizes multiple key decision variables, effectively reduces the complexity of the task offloading problem, and improves scheduling efficiency.

The invention discloses a composite material hot press molding production scheduling method based on a man-hour uncertainty dynamic transmission mechanism. In order to solve the problems that in the hot press molding process of the composite material, the working hours have uncertainty and are easily transmitted and amplified among multiple batches and multiple devices, a working hour uncertainty dynamic transmission mechanism is constructed, and the propagation rule of the workpiece machining time uncertainty between a batch layer and a machine layer is described; on this basis, constraints including an equipment capacity constraint and a workpiece and equipment matching constraint are comprehensively considered, a scheduling optimization model with minimization of the maximum completion time as an optimization target is established, and an optimization adjustment strategy is deduced; and finally, designing a mixed meta-heuristic algorithm to solve the scheduling optimization model in combination with an optimization adjustment strategy, thereby realizing efficient search of an optimal scheduling scheme. According to the method provided by the invention, the influence of uncertainty on the design of the production scheduling scheme can be effectively reduced, and the scheduling efficiency and stability of the composite material hot press molding process are improved.

The invention relates to the technical field of vehicle scheduling, in particular to an online car-hailing cross-platform transport capacity cooperative scheduling method and system. According to the method, semantic ablation and protocol escape of underlying data can be realized by acquiring parameters such as self-developed interface protocols, data formats and positioning precision of all platforms and building decentralized middleware, and abnormal data can be found and isolated in time by adopting double-link cross check and a multi-stage abnormal data judgment model; the dynamic weight calibration scheduling model is combined, more flexible and accurate transport capacity scheduling is provided based on real-time order data and factors such as positioning accuracy and data transmission stability of each platform, optimal matching between user requirements and optimal platform drivers is realized, and the scheduling efficiency and user experience of the whole system are improved.

The application discloses a bronchoscope supply-demand dynamic matching method based on multi-source data fusion, comprising: collecting multi-source heterogeneous data and performing fusion processing to obtain a device feature matrix, a demand feature matrix and an environment feature vector; determining a score calculation strategy according to a comparison result of a historical matching data accumulation amount and a preset threshold, determining a device availability score and a demand urgency score based on the device feature matrix and the demand feature matrix according to the score calculation strategy; constructing a device queue and a demand queue, attaching an initial scene label to each demand and performing identification, and outputting a standardized scene type; outputting an optimal matching scheme through a reinforcement learning intelligent agent based on the device queue, the demand queue, the standardized scene type and the environment feature vector; performing intelligent scheduling based on the optimal matching scheme, updating the queue based on external real-time events, and if both queues are non-empty at the same time, obtaining a new optimal matching scheme, otherwise, keeping a waiting state. The application can improve matching efficiency and accuracy.

This application discloses an artificial intelligence-based dynamic scheduling method and system for logistics transportation, relating to the field of artificial intelligence technology. It addresses the technical problem that existing technologies often rely on fixed rules based on experience or simple optimization algorithms, which become rigid in complex and dynamic real-world environments, unable to make forward-looking and global trade-offs, leading to low efficiency in transportation scheduling methods. The method obtains a dynamic scheduling knowledge graph by processing and integrating multimodal transportation data; optimizes the dynamic scheduling knowledge graph to obtain an optimized scheduling instruction set, and constructs a dynamic scheduling knowledge graph that transforms fragmented static information into a semantically related global dynamic relationship network, providing a panoramic and structured situational awareness foundation for decision-making. Based on this knowledge graph, rolling optimization scheduling is performed using an artificial intelligence model, enabling the learning of complex relationships, prediction of dynamic changes, and global trade-offs and adaptive decision-making, thereby improving the efficiency of transportation scheduling methods.

This application discloses a multi-agent collaborative scheduling system and method for power supply stations, relating to the field of multi-agent collaborative scheduling. The system includes: a master control agent subsystem, used to perform intent recognition, task planning, and task decomposition on service requests sent by the power supply station's front-end equipment, and to call service agents to execute decomposition tasks based on the A2A protocol; an MCP tool standard subsystem, used to encapsulate and manage data in the power supply station's database based on the MCP protocol to generate MCP tools; and a service agent subsystem, used to create service agents to call the MCP tool to read data stored in the power supply station's database, execute corresponding decomposition tasks based on the read data, and feed back the task execution results to the master control agent. This application enables multi-agent collaborative scheduling in real-world power supply station scenarios involving cross-systems, cross-role operations, long tasks, and strong compliance.

This application provides a firmware boot method applicable to the field of computer firmware technology. It includes: in response to a firmware boot command, executing a first coroutine task in a coroutine task queue; upon detecting that the execution of the first coroutine task has been paused by calling a blocking interface, saving context data determined during the execution of the first coroutine task to a first stack space corresponding to the first coroutine task; determining a second coroutine task in a paused execution state from the coroutine task queue; resuming the execution of the second coroutine task based on the context data stored in a second stack space corresponding to the second coroutine task; and determining the firmware state as booted based on the execution results of multiple completed coroutine tasks. This method utilizes cooperative coroutines to concurrently execute multiple hardware initialization tasks within a single thread, and achieves non-intrusive yielding by intercepting blocking interfaces, significantly shortening firmware boot time.

The application discloses a synchronous link data transmission method, a communication device and a storage medium. The method is used for synchronous link data transmission between a first device and a second device. The synchronous link has a reserved transmission window set at a preset interval. The first device generates a first service data block corresponding to the reserved transmission window in time sequence. The method is executed by the first device. The method comprises the following steps: in response to the fact that data transmission associated with a previous first service data block has been completed in a current transmission window, determining whether the current transmission window has sufficient remaining time; and in response to the fact that the current transmission window has sufficient remaining time, performing data transmission associated with a first service data block to be currently transmitted with the second device. The application can optimize the synchronous link of the pre-allocated time slice, improve the fragmentation problem of the time slice available for other links, reduce the link switching frequency, and improve the scheduling efficiency.

The application discloses a kind of configuration optimization method, device and system of integrated hydrogen production system, belong to hydrogen production technical field, the integrated hydrogen production system configuration optimization method provided by the present application, the two-stage configuration model of integrated hydrogen production system considering the uncertainty of photovoltaic output and biomass water content is established, the objective function is set as the minimization of investment cost and the operating cost of the worst scenario corresponding to the uncertainty set;Solve the above two-stage configuration model to obtain the optimized configuration scheme, thereby improving the robustness of configuration to ensure smooth hydrogen production.The scheme can ensure smooth hydrogen supply while meeting the system carbon emission requirements under photovoltaic, biomass access, reduce system component investment.In addition, the technical parameters involved therein relate to hydrogen production components and energy storage components, and the key parameters affecting the configuration results of system hydrogen production and energy storage can be subjected to sensitivity analysis.

This application provides a resource scheduling method, apparatus, electronic device, medium, and program product, which can be applied to the fields of big data technology and artificial intelligence technology. The method includes: acquiring multi-dimensional feature data; constructing an input feature vector based on the multi-dimensional feature data, wherein the multi-dimensional feature data includes at least task information and resource information; inputting the input feature vector into an interpretable model, outputting a scheduling mapping function, and determining a target scheduling factor based on the scheduling mapping function; performing hierarchical classification of tasks and resources based on the target scheduling factor to obtain a multi-dimensional combined classification result; and generating a corresponding first resource scheduling result using the interpretable model based on the multi-dimensional combined classification result.

The disclosure provides a method and device for receiving and transmitting information, in particular, provides a method performed by a user equipment (UE) in a wireless communication system and a UE performing the method. The method comprises: receiving a channel state information (CSI) reporting configuration; transmitting a first uplink channel, wherein the first uplink channel is triggered based on the CSI reporting configuration; and transmitting a second uplink channel associated with the first uplink channel, wherein the second uplink channel carries a differential layer 1-reference signal receiving power (L1-RSRP) associated with a reference signal resource pair indicated by a transmission configuration indication (TCI) state, wherein the differential L1-RSRP is determined based on a first threshold, and the first threshold is predefined or indicated by the CSI reporting configuration.

The invention relates to the field of robot scheduling, in particular to a resource collaborative scheduling method and system for multi-model beverage robots. The method comprises the following steps: collecting multi-batch beverage making tasks; carrying out equipment resource conflict identification based on the multi-batch beverage making tasks, and generating equipment resource conflict data; collecting real-time operation states of the plurality of beverage robots; performing state analysis according to the real-time operation state, and constructing a global working state diagram; performing overlapping degree calculation on the equipment resource conflict data to generate time sequence conflict points of the same equipment resource; performing collision risk quantification based on a global working state diagram and the time sequence conflict points to obtain a conflict task risk sequence; and performing multi-robot action scheduling planning on the conflict task risk sequence, and constructing an action collaborative scheduling strategy. According to the invention, through action cooperative scheduling, the resource utilization efficiency, the operation safety and the overall operation efficiency of the multi-model beverage robot are effectively improved.

This application provides a comprehensive energy optimization scheduling system and method based on big data analysis. It acquires historical and real-time monitoring data from multiple energy sources; identifies pattern similarity characteristics between renewable energy output curves and load demand curves from historical operating data; and divides the energy optimization scheduling cycle into multiple non-uniform scheduling periods based on these pattern similarity characteristics. It determines the predicted values ​​of renewable energy output and load demand, as well as the associated power deviation confidence intervals, for each non-uniform scheduling period. When the real-time power prediction deviation in a future non-uniform scheduling period exceeds the corresponding power deviation confidence interval, it reallocates power resources for the future non-uniform scheduling period, generating power adjustment instructions for global energy optimization scheduling; and performs comprehensive energy optimization scheduling based on big data analysis using these power adjustment instructions. The technical solution provided in this application can achieve optimized scheduling of comprehensive energy resources under the influence of power imbalance.

This application relates to the field of intelligent commodity management technology, and in particular to a commodity management method and system based on electronic tag technology and Internet of Things architecture. The method includes: establishing an intermodal correlation matrix for the tag data in the initial tag dataset of each commodity acquired through a reader, and obtaining the correlation coefficient between each tag data; the tag data is a received signal strength tag, a Doppler frequency shift tag, or a time difference of arrival tag; for each tag data, when the detected outlier value of the tag data is greater than a preset threshold, calculating a joint anomaly score for the tag data based on the outlier value of the tag, the correlation coefficient corresponding to the tag data, and the outlier values ​​of other tag data; when the joint anomaly score is greater than the preset threshold, deleting the tag data; and sequentially performing time-domain filtering, frequency-domain filtering, low-pass filtering, and standardization processing on the initial tag dataset after deletion to obtain a target dataset.

This invention relates to the field of real-time resource scheduling technology, specifically a real-time scheduling method for lane control robots based on multi-dimensional sensor data. The method includes: dividing management areas for arrival and drop-off robots; acquiring multimodal sensor data from the management areas, extracting features, and fusing them using a self-attention mechanism to obtain a fused feature vector; obtaining a drop-off scenario complexity score and a departure intention score through a drop-off scenario evaluation model; simultaneously calculating the real-time resource occupancy index for each lane; calculating differentiated allowed dwell time based on the drop-off scenario complexity score and the overall resource occupancy status of the drop-off platform; and providing early warnings and management based on the drop-off scenario complexity score, departure intention score, real-time resource occupancy index, differentiated allowed dwell time, and empty space detection results. This invention improves flexible decision-making capabilities and resource utilization, and is suitable for real-time resource allocation and decision support in complex traffic scenarios.

This invention discloses a multi-objective dynamic scheduling method and system for sanitation operation tasks, relating to the field of vehicle scheduling technology. It addresses the problem of how to achieve efficient and reliable dynamic scheduling of sanitation vehicles for unexpected tasks while considering road direction constraints and vehicle resource status. The method includes: acquiring the operational status information of multiple candidate sanitation vehicles; determining the operational connection anchor point and remaining workload of the original task for each candidate sanitation vehicle based on its operational status information and the road forward traffic rule network; determining a closed-loop path based on the operational connection anchor point, the location of the unexpected task, and the upstream connectivity graph of the road; determining the scheduling cost of each candidate sanitation vehicle based on its closed-loop path, real-time road conditions, remaining resources, and remaining workload of the original task; and selecting a target sanitation vehicle and issuing a scheduling instruction based on the scheduling costs of all candidate sanitation vehicles.

This invention provides a multi-source collaborative emergency power supply scheduling optimization method for coal mines based on supercapacitors, belonging to the field of coal mine safe power supply technology. The method includes acquiring real-time operating parameters of the power supply system to construct a node state feature matrix, decomposing and extracting dynamic features to construct a node priority system, generating power supply path scores and performing global optimization, determining equipment switching strategies and synchronous switching methods, and establishing a supercapacitor transitional power supply mechanism. This invention achieves rapid and reliable switching of coal mine power supply systems, ensures power supply continuity, and improves emergency response capabilities.

The application discloses a kind of regional power grid cooperation methods based on knowledge graph driving, comprising the following steps: collecting regional power grid data, generating multi-source heterogeneous power grid data set;Construct regional power grid knowledge graph, initialize federation heterogeneous graph neural network and graph neural controlled differential equation joint framework;Perform federation heterogeneous graph neural network coding, generate local embedding, mode weight decoupling parameter and local coding parameter;Run graph neural controlled differential equation modeling, generate continuous time state trajectory and dynamics parameter set;Perform federation aggregation on client set results, generate global model parameters;Issue global model parameters and update local model;Based on knowledge graph data, control path input and updated model parameters, generate regional power grid cooperation result set.The application significantly improves the cross-regional power grid cooperation prediction accuracy and calculation efficiency, effectively guarantees the operation safety and dispatching reliability.

This application discloses a power system dispatching method, apparatus, equipment, and medium based on line transmission quota tiered switching, relating to the field of power system operation technology. The method includes: constructing a continuous-time economic dispatching model and converting it into a finite-dimensional linear programming model through Bernstein polynomial interpolation; performing power flow calculation and verification on the sample set; combining historical data of renewable energy and loads; determining feature centers by clustering operation mode types; using a support vector machine classifier to identify the stability-instability boundary; eliminating unstable samples; dividing the power region of the remaining samples into grids; setting the maximum power value of each grid as the line transmission quota; calculating the distance between the renewable energy and load curves and each feature center over time; determining the category switching time point based on the time-varying distance; dividing the area into sub-intervals; selecting the minimum quota of the nearest sub-type as the time period quota; and generating a dispatching scheme to improve the efficiency and reliability of power system dispatching.

This application relates to the field of network technology, and provides a traffic scheduling method, apparatus, electronic device, and storage medium. The method includes: acquiring historical CDN traffic data, switch historical traffic data, and metropolitan area network (MAN) traffic data of network nodes; inputting the historical CDN traffic data, switch historical traffic data, and MAN historical traffic data into a traffic prediction model to obtain the predicted CDN traffic, switch predicted traffic, and MAN predicted traffic for future moments output by the traffic prediction model; determining the inbound traffic and outbound traffic of network nodes based on the predicted CDN traffic, switch predicted traffic, and MAN predicted traffic; and performing traffic scheduling based on the inbound traffic and outbound traffic. This method effectively avoids scheduling lag, achieves high scheduling accuracy, and, by using a model for traffic prediction, eliminates reliance on the experience of scheduling personnel, improving scheduling efficiency and reducing the cost of manual operation and maintenance.