67 results about "Scheduling function" patented technology

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 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.

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 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.

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.

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 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 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 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.

This invention provides a parking mapping operation method, apparatus, and electronic device. The method includes the following steps: when the main state machine receives a wake-up command, it starts a pre-created scheduled node in the mapping through a scheduling function pre-added to the autonomous driving operation framework. The scheduled node includes a mapping node and a positioning node; the mapping node obtains a first feedback signal indicating that the scheduled node has been started, wherein when the main state machine obtains the first feedback signal, the mapping node receives configuration data from the data collection node to complete a new mapping based on the configuration data; the mapping node obtains a second feedback signal indicating that the new mapping is successful; based on the second feedback signal, the vehicle is controlled to complete memory parking according to the new mapping, and the scheduled node is shut down after memory parking is completed. This improves the problem of excessive CPU computing resources being consumed and reducing the stability of the mapping function during memory parking mapping operation.

A diffusion strategy method and system for guiding multi-level safety constraint fields in embodied intelligent operation and maintenance robots is disclosed. First, a multi-level safety constraint field is constructed for equipment operation and maintenance scenarios, quantifying the safety risks of the safe operation zone, equipment sensitive access zone, personnel activity interference zone, and equipment protection prohibition zone in the form of a continuous differentiable scalar field. Then, a multi-modal state observation module for the operation and maintenance scenario is designed, integrating robot body state, target equipment geometry, and safety topology distance information. Next, the gradient of the multi-level constraint field is injected into each step of the diffusion strategy's denoising calculation using an embedded denoising guidance method, causing the denoising trajectory to deviate along the direction of decreasing constraint field value. Simultaneously, a learnable adaptive guidance intensity scheduling function is employed, outputting a larger guidance intensity in the macro-path planning stage and a minimal guidance intensity in the fine-grained control stage. Finally, an operation and maintenance action sequence that satisfies the multi-level safety constraints is output. This invention achieves an optimal balance between safety constraints and fine-grained control.

The present application relates to the technical field of data warehouse, especially to a data warehouse correlation link cleaning method, device, equipment and storage medium, the method first carries out script retrieval to the obtained task path set based on the preset function name matching rule, respectively to each function call line data obtained denoising and deduplication processing, obtain a plurality of task scheduling functions, based on a plurality of task scheduling functions in the preset data warehouse dictionary table SQL content search, obtain the SQL definition corresponding to each task scheduling function, respectively to each SQL definition character cleaning processing and sorting deduplication processing, obtain the data source table set corresponding to each task scheduling function and the cleaning target table set, the task path set, a plurality of task scheduling functions, a plurality of data source table sets and a plurality of cleaning target table sets are associated and bound and written in the blood relationship table, obtain the standardized blood table, aims at improving the working efficiency of data warehouse blood relationship carding.

The application discloses a kind of coroutine pool scheduling management method, system, device and storage medium.The method creates corresponding coroutine task according to the service demand of application program;Then, the coroutine pool of host thread is established and initialized;Subsequently, the host thread is controlled to obtain hosting core, to start the host thread;And the coroutine task in the host thread is executed by task scheduling function.The method generates corresponding coroutine task according to the service demand of application program, and allocates hosting core for host thread according to coroutine task, can effectively distinguish the demand of different data processing tasks, implement more targeted and more fine-grained coroutine scheduling and execution control, realize the isolation of host thread and non-host thread, implement differentiated scheduling control, for multi-core processor architecture, realize the global unified resource allocation management of multiple coroutine pool.The application can be widely applied in the field of communication technology.

The invention belongs to the field of power generation dispatching of a power system, and discloses a watershed water-wind-solar complementary regulation and control rule construction method and system for improving short-term flexibility support for a power grid. Based on a typical day peak and valley two-stage output process of a hydropower station, the short-term flexibility demand of a power grid is quantified by combining the adjustment capability and the adjustment duration; constructing a short time sequence simulation optimization model by taking the power grid demand as the constraint, and generating a hydropower electric quantity control boundary and a long-term monthly peak regulation function capable of reflecting the short-term peak regulation response characteristics of the system; on this basis, a long-term multi-target optimization model considering both power generation benefits and system peak regulation requirements is established, and water-wind-light long-term multi-target long-time-sequence optimization scheduling model calculation is carried out under long-term hydropower and electric quantity boundary constraints, so that the regulation capability of the watershed water-wind-light complementary system is improved; and finally, fitting a monthly scheduling function through a BP neural network, and determining an optimal long-term regulation and control rule of the watershed water-wind-solar complementary system in combination with an optimal energy storage interval calculation result.

Embodiments of the present disclosure provide methods, agents and computer programs for implementing control logic into a control platform, as well as a control platform and a technical system. A method of implementing control logic (38) into a control platform (22) for controlling a technical system (24) is described. The method comprises: receiving a query signal (50) from an input unit (30), where the query signal (50) represents a user query (28), and where the user query (28) describes at least one demand that the control logic (38) must meet; generating a function code (52) from the query signal (50), wherein the function code (52) comprises computer readable instructions determined according to the described requirements; determining a preliminary control logic (54) by allocating and scheduling the function code (52) on the control platform (22), wherein the allocated and scheduled function code (52) corresponds to the preliminary control logic (54); determining at least one profile of the preliminary control logic (54) by analyzing the assigned and scheduled function code (52); and optimizes the preliminary control logic (54) according to the profile until the demand is satisfied and the parallelized function code is retrieved, where the parallelized function code satisfying the demand represents the final control logic (38).

The utility model provides a kind of scheduling device and sample processing assembly line, scheduling device includes substance storage module, quality control article storage module, cleaning tube storage module, manipulator module, positioning transfer module and transfer transfer module, manipulator module can be transferred to positioning transfer module with sample test tube, cleaning test tube and quality control test tube;Transfer transfer module can send off scheduling device with test tube rack, positioning transfer module can be transferred to transfer transfer module with sample test tube, cleaning test tube and quality control test tube.The scheduling device of this embodiment can not only realize the scheduling of sample, but also realize the scheduling of quality control article and cleaning tube, the scheduling function of this scheduling device is more, can satisfy the scheduling demand of multiple articles of sample analysis assembly line.

This invention discloses a stress testing method and system for a large-scale task scheduling system based on a Kubernetes cluster, belonging to the field of container orchestration technology. The method first creates a simulation environment and deploys the large-scale task scheduling system on it; using the simulation node tool Kubemark, simulation nodes with the same computing resource specifications as real computing nodes are created in the simulated Kubernetes cluster, and the simulation node size is set; a task load management tool is used to create and terminate the stress testing task load of the task scheduling system; and a task data acquisition tool is used to collect task data in the real environment and collect task scheduling information in the simulation environment to achieve scheduling performance analysis. This method tests the scheduling function and performance of the large-scale task scheduling system, providing reference and verification for functional improvement and performance optimization of the scheduling system, reducing the testing and verification costs in large-scale computing scenarios, and ensuring the stability of the scheduling system.

Computer systems and methods are provided for virtualizing 5G services provided via satellite. A computer system includes at least one processor and at least one non-transitory memory storing instructions for execution by the at least one processor to implement a scheduling function and a network operations function, the scheduling function and the network operations function being communicatively coupled via an interface. The scheduling function is configured to control the operation of an antenna to provide radio access network (RAN) coverage to a plurality of cells in a target area. The network operations function is configured to configure the RAN coverage to serve the plurality of cells defined by at least one mobile network according to a set of network-related parameters and a set of satellite-related parameters.

Embodiments of the present specification provide a fault processing method and device, wherein the fault processing method comprises: in response to a fault event of a kernel module, determining a function call path corresponding to the kernel module; in the case that it is determined according to the function call path that the fault event belongs to a kernel scheduling function, determining a repair function in the function call path based on a target processor running in the kernel module, determining a historical state parameter of the target processor calling the repair function, and determining the historical state parameter as a current state parameter of the target processor; and scheduling a to-be-processed task to the target processor through the kernel module, wherein the target processor calls and executes the repair function based on the current state parameter to process the to-be-processed task.

The invention discloses an electric power data flow scheduling method for an electric power system, and belongs to the technical field of electric power communication networks. The method comprises the following steps: representing and classifying a power data stream by adopting a quintuple; mapping the data streams to different priority queues according to a classification result; establishing a scheduling function with the goal of maximizing the weighted throughput of the power business, and performing scheduling sorting based on multiple power business factors; and according to a sorting result, a time slot offset scheduling scheme is adopted to allocate transmission time slots for the power data streams. Furthermore, for data streams with high time synchronization requirements, zero-jitter transmission is realized by adopting a wait-free shaper. According to the method, the general TSN technology and the power business characteristics are deeply fused, and the problem that the multi-business differentiation and high-determinacy transmission requirements of the power system cannot be met in the prior art is solved through the full-process design from business identification, priority mapping, power special sorting to constraint-driven scheduling.

The application discloses a kind of based on digital twinning and giant caranx optimization algorithm's watershed water resources multi-objective intelligent scheduling method and system, belong to water resources management and optimization scheduling technical field, the method includes the basic geographic information of target watershed is collected, to construct the digital twinning model of the target watershed;Based on the real-time state field and runoff forecast output by the digital twinning model, construct multi-objective scheduling function;With the dynamic data output by the digital twinning model as input, under the preset constraint condition, the control parameters of the multi-objective scheduling function are iterated and optimized in parallel using giant caranx optimization algorithm, generate pareto optimal scheduling scheme set;According to the decision result selected from the optimal scheduling scheme set, issue control instruction to real physical watershed.The application realizes the deep integration of digital twinning real-time mapping and global optimization of swarm intelligence algorithm, forms the complete closed loop of "perception-optimization-elucidation-control", effectively solves the multi-objective conflict problem of water resources under complex geographical environment, improves the accuracy of runoff forecast while optimizing water resources utilization efficiency and ecological benefit.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a physical downlink control channel (PDCCH) communication scheduling a physical uplink shared channel (PUSCH) communication that is to include type-II Doppler channel state information (CSI). The UE may determine a slot offset between the PDCCH communication and the PUSCH communication based at least in part on a slot offset value to be used in association with transmitting type-II Doppler CSI. The UE may transmit the PUSCH communication based at least in part on the PDCCH communication and the slot offset. Numerous other aspects are described.

The invention discloses an elevator real-time monitoring fault diagnosis and intelligent operation and maintenance system, and relates to the technical field of elevators. According to the elevator real-time monitoring, fault diagnosis and intelligent operation and maintenance system, through associated arrangement of the data acquisition unit, the edge intelligent processing unit, the cloud collaborative diagnosis platform, the grading early warning unit, the operation and maintenance scheduling unit and the like, the problem that an existing elevator system cannot achieve comprehensive and timely troubleshooting of faults and subsequent overhaul and maintenance can be solved; specifically, full-dimension coverage of mechanical, electrical, environment and state parameters of the elevator is achieved through multi-source sensing fusion, and the composite fault recognition accuracy is improved; a collaborative architecture of edge real-time calculation and cloud deep optimization is constructed, the emergency fault diagnosis time is shortened, and the potential fault early warning advance is increased; the intelligent operation and maintenance scheduling function is integrated, full-process automation of fault positioning, personnel matching and spare part allocation is achieved, the maintenance response time is shortened, and the spare part allocation accuracy is improved.

The application discloses a skeleton action recognition method based on dynamic graph convolution fusing topological structure priori, and comprises the following steps: generating a topological barcode and converting the topological barcode into a continuous global topological feature vector through a differentiable mapping mode; constructing a node flow dynamic graph and an edge flow dynamic graph; extracting features of four modes of joints, bones, joint motion and bone motion, calculating topological confidence by using the global topological feature vector, and independently generating affine transformation parameters for each mode according to the confidence to obtain multi-modal joint features fusing topological priori; in the shallow stage of the network, the global topological feature vector is injected with high intensity to constrain the bone geometry structure, and in the deep stage of the network, the injection intensity of the topological information is gradually reduced through an inverse S-shaped continuous scheduling function, so that the network focuses on learning high-dimensional semantic discriminative features; and outputting an action recognition result; and the application can accurately distinguish complex or easily confused human behaviors.

The invention belongs to the field of material scheduling, and particularly relates to a material scheduling method and system based on a resource state. The method comprises the following steps: scheduling materials through a job task or a scheduling instruction issued by a scheduling class in combination with a transfer equipment resource abstraction class and a job team abstraction class; the scheduling class comprises scheduling functions and relationships between the job task and the transfer equipment resource and the in-warehouse job team abstract class; the transfer equipment resource abstraction class has transfer equipment attributes and a transfer equipment operation control method; the transfer equipment attributes comprise a transfer equipment state list and the current state of the transfer equipment; the transfer equipment state list comprises a transfer equipment operation state abstract class, and different newly added actions of the transfer equipment corresponding to the abstract class are expanded; the job team abstract class has team attributes and a job operation control method; the team attribute comprises a team state list and a team current state; the team state list comprises a team operation state abstract class, and different newly added actions of the team corresponding to the abstract class are expanded.

This application provides a human-vehicle interaction method for a two-wheeled electric vehicle, relating to the field of human-vehicle interaction. The method includes: receiving first interaction information input by a user through an interaction hub of a first device and parsing it into a scheduling command. The interaction hub is a processing unit for receiving interaction information, parsing the interaction information, generating scheduling commands, and scheduling function modules. The first device is an in-vehicle terminal device or a mobile terminal device communicatively connected to an in-vehicle terminal device. The interaction hub invokes at least one function module to execute the scheduling command according to the scheduling command, obtaining corresponding first result information. The function module is an independent business service unit connected to the interaction hub through a standard interface. The interaction hub outputs the first result information. The method provided in this application improves the efficiency and reduces the complexity of human-vehicle interaction.