136 results about "Scheduling function" patented technology

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 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 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 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 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 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 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.

The invention provides a web page generation method and device for a router, equipment and a storage medium, and the method comprises the steps: generating a corresponding web page index tree based on the web page addresses of all web pages of the router; based on the webpage index tree, generating a corresponding node tree by utilizing a scheduling function in a Lua language; generating a web page corresponding to the homepage of the router based on the web page index tree; wherein the web page corresponding to the homepage comprises an htm page corresponding to the homepage and a homepage navigation bar located at the top of the web page, and the homepage navigation bar comprises a plurality of navigation items; and in response to the detection that any navigation item of the homepage navigation bar is clicked, generating a web page corresponding to the clicked navigation item based on the web page index tree. According to the method, the webpage loading rate can be increased, and the user experience is improved.

The present invention discloses a multi-element fusion scheduling method and device for a computing power network, which relates to the field of computing power network resource scheduling. The method comprises the following steps: constructing a user objective function, converting a quantitative index into a target value U obtainable by computing power nodes, setting a coarse screening interval according to the target value U, constructing a system objective function, converting a system performance index into a target value S obtainable by computing power nodes, constructing a multi-objective fusion scheduling function based on NSGA II according to the user objective function and the system objective function, performing multi-objective coarse screening of computing power nodes using the multi-objective fusion scheduling function, obtaining all targets within the user coarse screening interval, performing personalized sorting based on all targets within the obtained user demand interval in combination with the user objectives and the system performance indicators, and selecting the optimal scheduling scheme according to the personalized sorting for computing power scheduling.

The invention belongs to the technical field of cloud edge collaboration, and particularly relates to a computing resource optimal configuration method oriented to cloud edge collaboration. Comprising the steps that K0, the operation sequence of data matrix operation examples is optimized through memory addresses and demand quantity sorting of the data matrix operation examples; k1, configuring a data memory and a shared memory in the computing service unit; k2, establishing a parallel data buffer area in the shared memory for pre-reading; k3, optimizing and matching the scheduling function to ensure that the computing service unit can realize better data multiplexing; and K4, optimizing a calculation work group load distribution strategy to improve the utilization rate of calculation resources. According to the computing resource optimization configuration method, the computing efficiency of the cloud edge cooperation task can be improved, the resource consumption of data matrix processing is reduced, and the cloud edge cooperation processing capability is improved.

The invention discloses an electric quantity scheduling method, device and equipment and a storage medium, relates to the technical field of energy management, is applied to a local end energy management system in a light storage scene, and comprises the steps of obtaining battery expected power and initial transaction expectation issued by a cloud; determining a target transaction expectation in combination with the initial transaction expectation according to a matching relationship between the current electricity price and a preset high electricity price condition; based on the matching relationship and the target transaction expectation, performing electric quantity scheduling on each energy storage device corresponding to the light storage scene by using the target power; the target power is the battery working power corresponding to the target transaction expectation determined according to the battery expectation power and the normal working power; the normal working power is the working power of the battery when the electric quantity is dispatched under the condition that the electric quantity transaction operation is not carried out. Therefore, the function of automatic power scheduling of the cloud can be played, the power scheduling strategy is optimized in combination with the actual situation of the light storage scene, and the use experience of a terminal user is improved through a more economical and real-time scheduling strategy.

The invention discloses a household energy storage scheduling strategy generation method and device, equipment and a storage medium. The method comprises the steps of obtaining a target scheduling instruction corresponding to a target user; based on a target template library, scheduling constraint information and the target scheduling instruction, determining a target scheduling function corresponding to the target user; and determining a target energy storage scheduling strategy corresponding to the target user based on the target scheduling function. According to the method, efficient generation of the household energy storage scheduling strategy can be realized, the efficiency and the flexibility of generation of the household energy storage scheduling strategy are improved, and the dynamic response capability to personalized requirements of a user is improved, so that the precision and the safety of household energy storage scheduling in a complex scene are ensured.

In some embodiments, a system can include a first stream to be opened with an arbiter device, the first stream associated with a first set of capabilities, a second stream to be opened with the arbiter device, the second stream associated with a second set of capabilities, and a wireless network architecture that determines a network messaging schedule based on the first set of capabilities and the second set of capabilities. The network messaging schedule can be sent to one or more client devices associated with the first stream or the second stream.

A distributed scheduling system includes a master scheduling platform and a slave scheduling platform. The number of the master scheduling platform and the number of the slave scheduling platform may be dynamically expanded respectively. When a back-end operating platform provides a scheduling data maintenance request to the master scheduling platform, the master scheduling platform provides scheduling data maintenance information to the slave scheduling platform according to the scheduling data maintenance request, so that the slave scheduling platform performs maintenance on scheduling data according to the scheduling data maintenance information. When a scheduling task execution condition of the scheduling data is met, the slave scheduling platform initiates a task, and calls a corresponding micro service to execute a business logic of the scheduling data. The distributed scheduling system and a distributed scheduling method realize highly available distributed scheduling functions.

This invention provides a method, apparatus, and electronic device for river water resource scheduling based on ecological flow, relating to the field of water conservancy engineering technology. The method includes: collecting regional water supply data using a river water intake and storage system; obtaining ecological flow data through downstream river ecological protection targets and a pre-stored ecological protection calendar; dynamically adjusting weights based on long-term hydrological data and the regional water supply data to obtain target weight coefficients; constructing a water resource scheduling optimization model based on the regional water supply data, the ecological flow data, and the target weight coefficients, wherein the water resource scheduling optimization model includes a multi-objective optimization scheduling function and constraints; and solving the water resource scheduling optimization model using a non-dominated sorting genetic algorithm to obtain the optimal scheduling scheme. This invention improves the accuracy of river water resource scheduling while ensuring river health.

This invention relates to the field of internet technology, specifically to a method and system for efficiently processing massive files based on cloud functions. The system includes a demand acquisition and cloud function locking module that acquires the business usage requirements of cloud users and locks the cloud functions and corresponding cloud function interfaces for the business files to be processed within those requirements. This invention deeply customizes cloud functions and object storage, reducing file upload and download processing, minimizing network impact, improving file processing speed, and reducing server consumption. File processing is event-triggered, and its business characteristics are suitable for serverless technology, providing on-demand supply. During periods of high-volume file processing, cloud function instances can be automatically scheduled to meet processing demands. Developers do not need to monitor business peaks and troughs, saving idle time costs and ultimately reducing operational costs. It also enables asynchronous processing of large batches of files, meeting the needs of high-concurrency business scenarios and achieving peak-shaving scheduling.

The application discloses a kind of multi-energy synergistic comprehensive energy storage power station wisdom management systems, and relates to energy storage power station wisdom management technical field;The application acquires heterogeneous energy storage equipment operation and loss data by energy storage asset state perception module, constructs differentiated transient stress loss mechanism to calculate switching loss cost by switching stress loss quantification module, multi-objective synergistic scheduling function is included in switching loss cost by synergistic scheduling generation module, and multi-energy synergistic scheduling decision is generated by Thailand particle swarm optimization algorithm, switching execution and trajectory optimization module plans power transition trajectory and inhibits transient stress, and asset degradation closed-loop correction module carries out closed-loop correction to loss quantification mechanism based on actual response data;Effectively solve the problem of large switching loss in energy storage power station scheduling, asset health and scheduling benefit imbalance, significantly improve the economy of power station scheduling and the service life of energy storage asset, realize the dynamic balance of scheduling strategy and asset protection.