168 results about "Scheduling function" patented technology

The invention, which relates to the warehouse management field, discloses an intelligent WMS system with an intelligent scheduling function, comprising a cargo sensing module, a data processing and analysis module, a scheduling decision module, a decision optimization module, and an execution control module. And the multi-agent scheduling module is used for defining each independent operation task in the warehouse as an agent, modeling the warehouse environment, and setting a reward function for each agent for guiding the agent to make an optimal decision. According to the intelligent WMS system with the intelligent scheduling function, the task completion time, the resource utilization rate, the cost saving and the service quality can be comprehensively considered through the set reward function, an intelligent agent can be guided to make an optimal decision, the accuracy and efficiency of future decision making can be improved through learning, and the intelligent WMS system has good application prospects. Therefore, the intelligent agent can gradually master the optimal scheduling strategy in different environments, and maximization of the overall warehouse operation efficiency is realized.

The invention discloses an agricultural equipment intelligent operation task scheduling method and system, and the method comprises the steps: building a multi-target scheduling function according to an agricultural equipment operation task type, equipment energy consumption cost, path planning cost, operation time constraint and a resource utilization rate; inputting the real-time environment data and the agricultural equipment state data into a deep neural network for learning and training to obtain an agricultural equipment dynamic collaborative optimization model for outputting a task demand predicted value; dynamically updating model parameters of the dynamic collaborative optimization model according to the farmland operation progress and the sudden working condition; and solving an optimal solution of the multi-target scheduling function by utilizing the updated dynamic collaborative optimization model, and generating a global optimal job task scheduling instruction. According to the method, the problem of intelligent agricultural equipment operation task scheduling under the multi-target condition is solved, and the real-time performance, the accuracy and the overall optimization effect of agricultural equipment scheduling are improved through a deep learning prediction model, a dynamic optimization algorithm and real-time environment data processing.

Certain aspects of the present disclosure provide techniques and apparatus for generating a response to a query input in a generative artificial intelligence model using variable draft length. An example method generally includes determining (e.g., measuring or accessing) one or more operational properties of a device on which inferencing operations using a machine learning model are performed. A first draft set of tokens is generated using the machine learning model. A number of tokens included in the first draft set of tokens is based on the one or more operational properties of the device and a defined scheduling function for the machine learning model. The first draft set of tokens are output for verification.

The invention relates to the technical field of charging pile management and power dispatching, and particularly discloses a charging pile operation management method and system. The method comprises the following steps: acquiring charging request data and power grid load data, and generating and cleaning a task vector data set; predicting a charging demand based on the data set and generating electricity price gradient information; a multi-objective optimization scheduling function is constructed and solved, and a task allocation scheduling scheme is generated; executing power control according to the scheduling scheme and collecting charging state data; executing dynamic contract charging and incentive generation according to the state data; and finally, performing Hash packaging and privacy processing on an execution result, and performing uplink to generate a credible record. According to the method, the resource allocation efficiency and the charging incentive intelligence level in a multi-user multi-period concurrent charging scene can be improved, and efficient cooperation and credible execution of the whole charging scheduling and settlement process are realized.

The invention relates to the technical field of simulation regression systems, and provides a parallel simulation regression method and system based on cluster servers. The method comprises the following steps: classifying simulation tasks according to task types, and determining a dependency relationship among a plurality of simulation tasks; generating a parallel simulation task list through a cluster server management tool according to the dependency relationship among the plurality of simulation tasks; and delivering the parallel simulation task list to a cluster server, and running simulation tasks by the cluster server to generate a simulation result. The parallel simulation regression system is conveniently constructed by utilizing the scheduling function of the cluster server, the dependency relationship of various types of tasks can be processed, the degree of parallelism of the simulation regression system is increased to the maximum extent, and the method has universality and can be widely applied to different scenes.

The invention discloses an embedded AI intelligent computing power architecture method, and particularly relates to the technical field of artificial intelligence processing architecture. Collecting a resource state parameter set R of the embedded device; obtaining a to-be-executed AI task set T, wherein each task comprises model complexity, real-time requirements, expected response duration and priority; constructing a computing power resource allocation evaluation function F based on R and T, and outputting a task scheduling priority score; allocating tasks to the embedded AI module according to the allocation scheme and executing reasoning; the resource state is dynamically monitored in the task running process, and if it is predicted that resources are about to be overloaded, a scheduling function F is triggered to reconstruct a resource allocation scheme; performing iterative optimization on weight parameters in the function F based on task history feedback; by means of the method and device, optimal adaptation of multi-task concurrent scheduling can be achieved under the condition that resources are limited, the computing power resource utilization rate, the response efficiency and the system stability are improved, and the method and device are suitable for various edge side AI scenes.

The invention discloses an unmanned aerial vehicle task scheduling method and device, electronic equipment, a medium and a product. The method comprises the following steps: acquiring all to-be-executed tasks in the remote control system; uniformly registering the to-be-executed tasks into a pre-constructed task table; wherein the unified scheduling entry of the task table is a preset main scheduling function; triggering a new task scheduling cycle according to the minimum time beat of the system; preferentially executing the currently triggered target key event according to the function hook triggering condition of the key event; and after the target key event is processed, according to the main scheduling function and the scheduling frequency registered in the task table, the remaining non-core common tasks in the task table are executed in sequence. The method can solve the problems that a key radio frequency event is not responded in time and data is easy to lose.

An electric vehicle charging and discharging scheduling method can include setting a scheduling model based on a constraint function and an objective function considering a contract for difference, smart charging, a plus demand response (DR), and a national DR, optimizing the set scheduling model, and performing electric vehicle charging and discharging scheduling using the optimized scheduling model. The constraint function can include a function for at least one of restrictions on charging / discharging for each electric vehicle. The objective function can include a regular scheduling function considering the contract for difference and the plus DR market, and an irregular scheduling function considering the contract for difference, the plus DR market, and a national DR market.

The invention provides a ship berthing method and system, and the method comprises the steps: enabling an applicant to log in the ship berthing system, and submitting a berthing application text; and the ship berthing system extracts key information in the berthing application text and automatically generates a ship berthing application form. And pushing the ship docking application form to a client of an approver, approving the ship docking application form by the approver, and generating an approval result. Visiting ship information is obtained in real time, the ship information is input into the ship berthing system, and the ship berthing system generates a ship dispatching scheme. And feeding back the approval result and the ship scheduling scheme to an applicant and a wharf manager client. The ship berthing system integrates application, approval and scheduling functions, so that the time for manually filling in an application form and approval is shortened, and the approval efficiency is improved.

The invention relates to the field of multi-energy coupling complementary scheduling, and discloses a watershed water-wind-light dynamic adaptability aggregation decomposition scheduling rule compilation method, which comprises the following steps: S1, constructing a multivariable joint stochastic simulation model, and jointly reconstructing a water-wind-light non-consistent combination scene; the method comprises the following steps: S1, solving an optimal scheduling trajectory by adopting a successive approximation dynamic programming method, S3, based on an aggregation and decomposition theory, respectively selecting appropriate independent variable factors and scheduling decision factors for an aggregation system (an integrated system) and a decomposition system (a water-wind-light-bundled subsystem in the integrated system), and S4, based on a water-wind-light scene under a non-consistency condition, carrying out scheduling on the water-wind-light-bundled subsystem in the integrated system. S5, taking the watershed water-wind-light non-consistency scene combination as the input of the model, and applying the EnKF-KNN process in the S4 on the basis of the static scheduling function type determined in the step S3, taking a static scheduling function parameter as an initial value, and applying a data assimilation and machine learning coupling model.

The embodiment of the invention provides a missing page recovery method and device and a storage medium. A new IOPF scheduling function is innovatively provided in the virtual machine manager. The method comprises the following steps: determining a target virtual machine to which a missing page request belongs in response to monitoring of the missing page request initiated by any IO device on a physical machine; selecting a target physical kernel used for processing the page missing request for the target virtual machine from the plurality of physical kernels; and scheduling the missing page request to the target physical kernel so as to process the missing page request by utilizing the target physical kernel. Therefore, by determining the virtual machine to which each IOPF occurring in the physical machine belongs, the IOPF scheduling can be carried out by taking the virtual machine as a constraint condition, so that the IOPF can be prevented from being concentrated on a certain specified physical kernel on the physical machine to be processed, and disturbance of IOPF scheduling to irrelevant virtual machines can be effectively avoided, thereby ensuring reasonable scheduling of the IOPF and further improving the processing efficiency of the IOPF.

The invention discloses a three-dimensional reconfigurable hardware acceleration core chip, belongs to the technical field of chips, and is characterized in that the three-dimensional reconfigurable hardware acceleration core chip comprises a reconfigurable operation array used for providing at least one unit-level calculation unit and at least one algorithm-level calculation unit; the storage array is used for storing operation data input by the AXI bus and output by the reconfigurable operation array; the controller set is used for controlling the at least one cell-level computing unit and the at least one algorithm-level computing unit so as to achieve cell-level computing operation and algorithm-level computing operation respectively and controlling operation data storage of the storage array. The three-dimensional reconfigurable hardware acceleration core chip based on static scheduling and a static data flow model is constructed by managing scheduling functions such as configuration decoding, reconfiguration control, calculation control, data distribution and storage control through an independent control system, and the chip realizes multi-dimensional reconfiguration of a space dimension, a time dimension and a resource dimension through storage and calculation decoupling.

The invention provides a digital resource management system and method. The system comprises a digital resource acquisition module, a digital resource processing module, a digital resource management module and a digital resource sharing service module. Aiming at digital resources such as project resources, application resources, data resources, service resources, cloud resources and component resources, the system realizes the functions of one-stop browsing, one-package application, one-account management, integrated operation and maintenance, one-network management and control and one-platform scheduling, and constructs a unified account book, unified service, unified management, unified operation and maintenance, unified scheduling and unified portal of the digital resources. A digital, visual, processized and closed-loop service system, a management system and a support system are established, multi-dimensional and multi-level real-time analysis of digital resources is realized, and mechanism service work is powerfully supported; full-life-cycle management of digital resources is achieved, management is more systematized and comprehensive, the management level and use efficiency of the digital resources are effectively improved, and repeated construction and waste of the resources are avoided.

The invention discloses a geophysical network multi-algorithm fusion data processing method and system, and relates to the technical field of geophysical information processing, and the method comprises the following steps: S1, constructing a micro-service cluster based on a SpringCloud framework, respectively deploying data conversion, data preprocessing, conventional product calculation, data visualization and task scheduling functions as independent micro-services, and an elastic service architecture with a transverse expansion capability is established to support parallel cooperative operation of each function service. Through micro-service architecture, an intelligent recognition engine, dynamic visualization, security access control and operation monitoring closed-loop management, the automation, intelligence and security of geophysical data processing are comprehensively improved, and the system has the advantages of high concurrent processing, quick response, expandability, high reliability and the like, and effectively supports earthquake monitoring and geophysical service requirements.

The invention provides a multi-modal optimal noise scheduling-based molecular generation method and device, and the method comprises the steps: representing any one joint noise scheduling function in a predefined joint noise scheduling function space as a preset target form through a time re-parameterization function, and building a unified parameterization frame of continuous modal noise and discrete modal noise; minimizing a pre-constructed generalized loss function through a pre-trained model, and training based on the generalized loss function to obtain a generation model for evaluating any joint noise scheduling function in the joint noise scheduling function space; based on an L2 norm square error evaluation result of each point of the discretized time grid by the generative model, searching and determining a minimum cumulative cost path from a starting point to an end point through a dynamic programming algorithm; and determining optimal joint noise scheduling according to the minimum cumulative cost path, and generating a drug molecular structure based on the optimal joint noise scheduling. Through the method provided by the invention, the geometric effectiveness and the calculation efficiency of molecule generation are improved.

The invention relates to the technical field of smart home, and discloses a deep learning task allocation method and device, and electronic equipment, and the method comprises the steps: analyzing heterogeneous computing resources of each processor in target equipment, and obtaining an analysis result; determining a target factor according to the demand of the to-be-allocated deep learning task and the resource condition of the target device; according to the parameter value corresponding to the target factor and the task scheduling function, calculating the priority of the to-be-allocated deep learning task to obtain a calculation result; according to the analysis result and the calculation result, distributing the deep learning task to a target processor; the target processor is provided with a compressed large model and is used for executing a deep learning task. And task allocation is performed according to the analysis result and the calculation result, and the deep learning task is allocated to the most suitable processor, so that the execution efficiency of the deep learning task is improved.

The invention discloses a method and a system for generating and updating an atlas of a BOM table in prototype design and research and development, and the method comprises the following steps: regularly extracting BOM data of a main material and a sub-material from a source database, establishing a corresponding relation and a quantity detail table of the main material and the sub-material, and obtaining an atlas of the main material and the sub-material by adopting SQL query and recombination of a material hierarchical structure; converting the data according to the hierarchical relationship of the materials, generating a plurality of hierarchical conversion tables of the main materials and the sub-materials, and loading the tables into a target SQL Server database; database connection is established in Excel, a hierarchy conversion table is updated and inquired, and data of each hierarchy of BOM is extracted and put into a plurality of worktables to be displayed respectively; performing multi-row-to-multi-column reconstruction on the single-level BOM data respectively displayed in the plurality of worktables according to a hierarchical structure, and finally summarizing the single-level BOM data into the same multi-level BOM table; material information of each level is extracted from the multi-level BOM table at regular time, node relation data is automatically generated through conversion and job scheduling functions of Kettle, and the node relation data is updated to a Neo4j database at regular time; and according to the node relation information in the Neo4j graph database, using a D3 force oriented graph to realize the graphical display of the multi-level BOM table. According to the method, the automation degree and the visualization effect of BOM data processing are improved.

The invention belongs to the technical field of network security, and particularly relates to an execution body scheduling optimization method and device for a mimicry defense framework, and the method comprises the steps: forming a plurality of execution body sets through employing a plurality of execution bodies corresponding to a to-be-tested source program; calculating the isomerism degree of each executive set and the security defense coefficient of each executive set; calculating to obtain a scheduling function score of each executive set by utilizing the isomerism degree of each executive set and the security defense coefficient of each executive set, and determining a final scheduling object according to the scheduling function score of each executive set; and according to the judgment result of each execution body in the final scheduling object in the mimicry defense framework, updating the number of the execution bodies scheduled from the final scheduling object during each scheduling. According to the technical scheme provided by the invention, the scheduling efficiency is optimized while the maximization of the difference between the executors is ensured, and the safety and the performance of the system are improved at the same time.

The invention relates to the technical field of mobile computing power networks, and provides a computing power network session management system, method and device, electronic equipment and a storage medium, a base station receives an uplink service data packet sent by user equipment, and determines a session identifier and a target IP address; if the session identifier is a computing network session identifier and the target IP address is the IP address of the base station, the base station executes a task corresponding to the uplink service data packet as a computing power node; otherwise, packaging the uplink service data packet; wherein the target IP address is determined by a computing power management scheduling function module and a core network; and the core network de-encapsulates the encapsulated uplink service data packet and then sends the de-encapsulated uplink service data packet to a server corresponding to the target IP address, and the server serves as a computing power node to execute a task corresponding to the uplink service data packet. According to the method and the device, the specific session identifier is allocated to the computing network session, and whether the base station is used as the computing network session endpoint to execute the task corresponding to the uplink service data packet is judged according to the session identifier and the target IP address, so that the management of the computing network session is realized.

The present application relates to an electric vehicle charging and discharging scheduling method and apparatus, a device, a medium, and a product. The method comprises: by taking the lowest total operation cost of a target EVA in an energy market as an objective, constructing a target scheduling function corresponding to the target EVA; determining a constraint condition of the target scheduling function, wherein the constraint condition is used for constraining the charging and discharging power and energy output of the target EVA; on the basis of the constraint condition and the target scheduling function, determining charging and discharging powers of the target EVA at different moments; on the basis of the charging and discharging powers of the target EVA at different moments, and power state data of electric vehicles having deployed under the target EVA at corresponding moments, determining a scheduling strategy of the target EVA, wherein the power state data comprises at least one of state of charge, battery charging power, and battery discharging power; and according to the scheduling strategy of the target EVA, performing charging and discharging scheduling on the electric vehicles having deployed under the target EVA. The use of the method can reduce the total operation costs of an EVA.

The invention discloses an automatic test script generation method and device based on deep learning, equipment and a medium, and relates to the technical field of computers. Comprising the following steps: analyzing an obtained test case to extract key test information; using a preset dynamic Bayesian classification model to perform classification evaluation on the test cases based on the key test information to obtain corresponding risk probabilities, and using a preset weight adjustment function to adjust the priorities of the test cases according to the risk probabilities; generating an automatic test script based on the test case by using a preset deep learning model; extracting failure features from the execution result of the automated test script by using a preset failure mining function, and updating the failure features to a preset knowledge base; and utilizing a preset incremental learning scheduling function to optimize a preset dynamic Bayesian classification model and a preset deep learning model based on the execution result. Therefore, intelligentization, high efficiency and sustainable optimization of automatic test script generation can be realized.

The invention discloses a business collaborative scheduling method and device suitable for a decentralized multi-cluster Internet of Things, and belongs to the technical field of Internet of Things scheduling, and the method comprises the steps: deploying an algorithm in a control management scheduling function module on a cluster head node of each cluster of the multi-cluster Internet of Things, and the control management scheduling function module on each cluster head node calls an algorithm to complete an online processing process among a plurality of service requests so as to deploy the dynamically arrived service requests. The method can effectively prevent privacy leakage, and has the advantage of high deployment speed.

The invention provides a computing power resource scheduling management system, method and device and a medium, and belongs to the technical field of computers. The system comprises a scheduling overview module which is used for providing an operation topology overview and scheduling task overview analysis function for computing power resources; the scheme center module is used for providing the functions of querying, stopping and checking details of the capability service of the computing power resources and carrying out full-life-cycle management on a scheduling strategy; the scheduling center module is used for providing analysis, arrangement and scheduling functions for scheduling tasks of the computing power resources; the sensing center module is used for providing functions of sensing and managing computing power resources; and the security center module is used for providing security management and auditing functions in the task scheduling process. According to the method, dynamic perception and scheduling of computing power resources are realized, the resource utilization efficiency and the response speed are improved, and meanwhile, the safety and the reliability of the system are enhanced.

The invention provides a scheduling method of a water-wind-light complementary power generation system based on a knowledge graph structure, and belongs to the technical field of hybrid energy scheduling. According to the method, a scheduling rule structure of the water-wind-light complementary power generation system is designed based on an object-time period-situation-decision knowledge graph, an optimizable variable set in a knowledge graph rule is extracted, a knowledge graph scheduling rule optimization model of the water-wind-light complementary power generation system is established, and solving is completed; and proposing a graph chain reasoning method for obtaining a decision result based on the current scheduling situation, and obtaining a scheduling scheme calculated by the water-wind-light complementary power generation system according to the knowledge graph rule. Compared with a rule in a traditional scheduling function form, the knowledge graph scheduling rule has the advantages that the generating capacity is higher, the satisfaction degree of the power grid load is higher, meanwhile, the knowledge graph scheduling rule can process the multi-target scheduling requirement, and the knowledge graph scheduling rule has the advantages of being good in intuition, high in interpretability, flexible, easy to expand and the like; the method is of great significance to operation and decision-making of the water-wind-light complementary power generation system.

The invention relates to a digital cutter management system with intelligent production scheduling, and aims to optimize cutter use and management in industrial production. The system comprises four main modules: a cutter basic management module, an inventory management module, an interface module and a production management module. The tool basic management module is responsible for managing 2D and 3D graphic information of a tool and monitoring the service life of the tool in real time. The inventory management module uses a bar code or a two-dimensional code to track in-out of a tool, automatically calculates a purchase demand, reminds a minimum inventory level, and supports full-process management of a tool list. And the interface module realizes data exchange with various numerical control programming software and enterprise-level management systems. The production management module analyzes the data of the production control system, extracts a production task and a tool demand list, analyzes tool consumption, generates a purchase plan, and adjusts a production plan according to tool life data and inventory conditions. According to the system, the production efficiency and the cutter utilization rate are greatly improved through integrated management and intelligent characteristics.

The present invention relates to an ATS system reconstruction method, and an electronic device and a medium. The method is implemented on the basis of an ATS system, an FSK-LAN protocol conversion module and an interlocking system, wherein the ATS system is responsible for receiving device state information from the interlocking system that is forwarded by the FSK-LAN protocol conversion module, sending a control instruction to the FSK-LAN protocol conversion module, and providing a human-machine interface for displaying and controlling the operation of a signal system; the FSK-LAN protocol conversion module is responsible for modulating a digital signal of the ATS system into an electrical signal, forwarding the electrical signal to the interlocking system, modulating an electrical signal of the interlocking system into a digital signal, and forwarding the digital signal to the ATS system; and the interlocking system executes the control instruction forwarded by the FSK-LAN protocol conversion module and sends to the FSK-LAN protocol conversion module the device state information from the interlocking system. Compared with the prior art, the present invention can realize networked access for a new system and existing systems of ATS while entirely retaining station equipment, thus quickly reconstructing a scheduling function of an ATS system.

The invention discloses a forest seedling raising period management optimization method based on an adaptive algorithm. The method comprises the following steps: S1, collecting multi-source environment data and seedling growth state data in a forest seedling raising process; s2, the original index data set is processed, a standardized index matrix is obtained, and a stage index evaluation input set is constructed; s3, constructing a multi-target periodic scheduling model based on the stage index evaluation input set; s4, introducing an entropy weight adaptive grey wolf optimization algorithm to solve the multi-target scheduling function set, and outputting a periodic scheduling optimal solution; s5, generating a seedling culture management execution strategy based on the period scheduling optimal solution; s6, forming a feedback monitoring data set by the environment feedback data collected in real time and the seedling feedback data, and generating an updated seedling management execution strategy; and S7, synchronizing the updated seedling culture management execution strategy to the seedling culture management platform. According to the invention, intelligent scheduling and adaptive management optimization of the whole forest seedling raising process are realized.

The invention provides an automatic detection method for communication network equipment, which comprises the following steps of: identifying a key node of a key link in a service process, and acquiring state data of the key node from the communication network equipment in real time; performing vectorization processing on the key node state data to obtain a service feature vector; marking that the processing capability of the corresponding key node fluctuates; analyzing the key node state data according to a time sequence to obtain a change trend of a service feature vector; if the change trend shows that the node load rate continuously rises or the response time distribution continuously increases, generating early warning information; matching the early warning information with a performance correction rule, and correcting a resource configuration function and a service scheduling function of the equipment; judging whether the correction measures are valid or not, and if yes, updating the performance evaluation result; and if not, triggering a function early warning mechanism and adjusting a performance correction rule. According to the invention, the intelligent and adaptive optimization of the performance of the network equipment can be realized.

We provide the system. [Solution] Means for analyzing messages, A means for automatically generating a response based on the analyzed content, A means for presenting the generated response to a terminal device, A method for presenting responses along with suggested dates and times in conjunction with the scheduling function, A means of analyzing public notices and assessing their urgency, Means of providing special warnings according to the urgency, A system that includes this.

The application discloses an embodied intelligent Agent system for an industrial scene and an implementation method thereof, and the system comprises: a user interaction layer: used for providing diversified interfaces and communicating with a backend service through an API gateway; an embodied intelligent layer: used for adopting a micro-service architecture based on container arrangement to realize task management, task planning and resource scheduling functions; and a physical device layer: used for integrating with the upper layer system through an industrial Internet of Things protocol and processing time-sensitive applications by adopting an edge computing technology; wherein the user interaction layer and the embodied intelligent layer are bidirectionally connected, and the embodied intelligent layer and the physical device layer are bidirectionally connected. The application can independently perform a series of operations such as order analysis, material planning, production planning, task distribution, material preparation, production execution, quality control and abnormality processing, greatly reduces manual intervention and improves production efficiency.