107 results about "Scheduling function" patented technology

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.

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 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 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 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.

The invention discloses an energy storage scheduling strategy determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring target voice information of a user; inputting the target voice information into a pre-trained energy scheduling model for strategy generation, and determining an energy storage scheduling strategy based on the output of the energy scheduling model; wherein the energy scheduling model comprises a constraint identification module, a scheduling function determination module and a scheduling strategy determination module, the constraint identification module is used for determining scheduling constraint information according to the target voice information, and the scheduling function determination module is used for determining a target scheduling function according to the scheduling constraint information; and the scheduling strategy determination module is used for generating a strategy according to the scheduling constraint information and the target scheduling function. According to the invention, efficient generation of the energy storage scheduling strategy can be realized, and the flexibility and interpretability of determination of the energy storage scheduling strategy are improved, so that the precision and security of energy storage scheduling in a complex scene are ensured.

The application provides a method and system for switch testing using digital twin technology, including the following steps: using digital twin technology to construct a digital twin model of the switch, including a hardware model, a software model and a network topology structure model of the switch; based on the digital twin model, designing a function test scene of the switch, including a VLAN configuration, a cross-switch link aggregation and a multi-path scheduling function test scene; using an automated test tool to write an automated test script and inserting a test case into the automated test script; running the automated test script, performing black box testing on the digital twin model, and recording the execution result of the test case; comparing the execution result with an expected result, determining whether the test case passes, and for a test case that does not pass, performing error tracking and positioning, locating and repairing problems in the switch. Various different workloads and scenes are simulated in the digital model, thereby improving test coverage and test effect.

The invention discloses a programming method, device and equipment based on a producer-consumer model. The method comprises the following steps: packaging a production instruction of a producer for producing each data sub-block into a production function; wherein an original data block of interaction between the producer-consumer model is divided into a plurality of data sub-blocks in advance; packaging a consumption instruction of consuming each data sub-block by a consumer into a consumption function; and calling the production function and the consumption function through a scheduling function based on a preset flow line initial batch number and a batch processing sub-block number, and realizing flow line parallelism of production and consumption of the data sub-blocks. According to the technical scheme, through packaging of functions of data production and data consumption and calling of functions of parallel production and consumption, programming errors caused by human errors of programmers can be effectively reduced, the programming overhead of the programmers can be effectively reduced, and the programming efficiency is improved.

The application provides a kind of scheduling method of water, wind and light complementary power generation system based on knowledge graph structure belongs to mixed energy scheduling technical field.The application designs water, wind and light complementary power generation system scheduling rule structure based on "object-time period-situation-decision" knowledge graph, extracts the variable set that can be optimized in knowledge graph rule, establishes water, wind and light complementary power generation system knowledge graph scheduling rule optimization model and completes solution, proposes the graph chain reasoning method based on current scheduling situation to obtain decision result, obtains the scheduling scheme calculated according to knowledge graph rule of water, wind and light complementary power generation system.The knowledge graph scheduling rule of the application is compared with the rule of traditional scheduling function form, and the power generation capacity is higher, the satisfaction degree to power grid load is higher, meanwhile, the knowledge graph scheduling rule can process multi-objective scheduling demand, has the advantages of good intuitiveness, strong explainability, flexible and easy to expand, etc., and has important significance for operation and decision of water, wind and light complementary power generation system.

The application provides a UAV scheduling method, system and readable storage medium, the method comprising: predicting whether the target UAV has insufficient power when performing an unfinished task according to the current power; if the target UAV has insufficient power, constructing a spatial constraint condition; obtaining a candidate UAV set and a candidate task handover point set, combining any candidate UAV with a candidate task handover point to determine whether the combination result satisfies the spatial constraint condition; obtaining a first candidate set according to the determination result, the first candidate set comprising at least one selected UAV and at least one selected task handover point corresponding to each selected UAV, and constructing a linear scheduling function to screen a first replacement UAV and a first handover point corresponding to the first replacement UAV from the first candidate set according to the linear scheduling function. The application greatly improves the stability, safety and efficiency of UAV task execution by predicting the power, reasonably screening and optimizing the task scheduling.

The application discloses a non-strict alignment low-resource helium-oxygen voice recognition method and system, relates to the technical field of voice signal processing, and comprises the following steps: obtaining to-be-recognized helium-oxygen voice data, extracting the to-be-recognized helium-oxygen voice data, obtaining a time length feature, obtaining a linear resampling factor through a preset continuous scheduling function based on the time length feature, performing linear resampling processing on the to-be-recognized helium-oxygen voice data based on the linear resampling factor, and obtaining to-be-recognized preprocessed voice data; inputting the to-be-recognized preprocessed voice data into a pre-established helium-oxygen voice recognition model, outputting a voice recognition result, and improving the voice recognition accuracy.

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

The application belongs to the technical field of intelligent traffic, and discloses a simulation system and method applied to intelligent bus emergency failure scheduling, which comprises the following steps: a bus-mounted system collects bus position information, vehicle operation state information and real-time statistics of the number of passengers getting on and off the bus; a bus scheduling simulation system comprises a scheduling management platform and a scheduling simulation platform, the scheduling management platform records real-time passenger flow data, manages historical passenger flow data and vehicle operation data, runs an emergency failure scheduling algorithm, and outputs a driving plan, and the scheduling simulation platform is a simulation environment of the emergency failure scheduling algorithm; a bus station system collects station passenger flow data and passenger waiting time of a bus station; and a bus station system collects bus departure interval data and bus departure number of a bus station. The application solves the problem of lacking simulation schemes for intelligent bus emergency failure scheduling functions in the bus industry.

The application provides a program scheduling system and method of a multimedia display screen, a storage medium and an electronic device. The system comprises: a signal sending platform, configured to provide a program template arrangement function, a program list issuing function and a program scheduling template version management; a C-end application program, configured to receive a program scheduling template and a program list issued by the signal sending platform, and perform basic communication and program playing; a JSSDK module, configured to provide a two-way communication protocol interface between the C-end application program and the program scheduling template; a program scheduling template, configured to acquire a program list through the JSSDK, perform scheduling logic, and send a playing instruction to the C-end according to a preset rule; and a program template, comprising playable multimedia materials, arranged by the signal sending platform and presented through the C-end. The application realizes that only one set of code needs to be maintained, various types of C-ends can realize the program scheduling function, and unnecessary repeated work of C-end development is reduced.

The invention discloses a large file data analysis processing method. The data analysis processing method comprises a data access stage; a distributed fragmentation processing stage; a reserved interface implementation stage; a fault tolerance and breakpoint recovery stage; and a frame adaptation and expansion stage. A Spark distributed computing framework is adopted to replace MapReduce, Spark computes based on a memory, the processing speed is higher in an iterative computing scene, the functions of data fragmentation, parallel processing and task scheduling are achieved, and the effect of improving the processing efficiency can be achieved.

A time-domain scheduling method, apparatus, and system belong to the field of communication technology. The method is executed by a control node in an FTTR network. In this method, the control node sends an enable instruction message to a first network node to indicate whether to enable the time-domain scheduling function, and receives an enable confirmation message from the first network node confirming the enable of the time-domain scheduling function. The first network node is any one of at least one network node in the FTTR network. The enable confirmation message indicates whether the first network node has enabled the time-domain scheduling function. This application helps avoid random backoff conflicts on the air interface and can improve transmission efficiency in the network. This application is applicable to FTTR networks.