16results about How to "Improve schedulability" patented technology

The invention discloses an intelligent self-adaptive real-time task scheduling method and system based on multi-dimensional optimization. The method comprises the following steps: S1, inputting a task; s2, a DAG task is decomposed into independent sequential sub-tasks; s3, dynamically calculating the priorities of the tasks by adopting a multi-factor dynamic priority scheduling module, and updating the priorities of the tasks; s4, dynamically adjusting an acceleration factor by adopting a self-adaptive acceleration factor management module; s5, vertex correlation analysis is carried out, and a variance of processor load imbalance is calculated by adopting a load balanced distribution algorithm and is minimized; s6, in combination with static analysis and dynamic inspection, a hybrid schedulability judgment module is adopted to judge the tasks, and judgment results are fused; and S7, fusing decisions by adopting a decision fusion algorithm, obtaining a scheduling strategy through a scheduling strategy coordinator, executing the task, and monitoring the task in real time. Real-time monitoring and real-time optimization scheduling are realized, and the utilization rate of resources is improved.

The invention discloses a 5G + TSN fusion network asynchronous flow optimization scheduling method with high schedulability, which comprises the following steps: S1, in a 5G and TSN fusion network, firstly, a controller of a center collects overall information of the network through a network control protocol, the information comprises channel conditions and frequency resources in the 5G network, and the channel conditions and the frequency resources in the 5G network are transmitted to a server; the communication requirements of the network topology, the link rate and the flow of the TSN network are met; and S2, running an asynchronous traffic optimization scheduling model of the 5G + TSN hybrid network through the central controller, performing routing decision and transmission scheduling on the traffic collected in the previous step in the hybrid network to obtain a scheduling result, and minimizing the consumption of network resources while meeting the transmission requirement of hard real-time traffic. According to the invention, the schedulability of the TSN gateway is obviously improved, the application range of an asynchronous deterministic access mechanism is expanded, and end-to-end traffic scheduling is realized.

The invention provides a resource scheduling method, device and equipment for a charging station, which can be applied to the technical field of new energy automobile and power grid interaction. The method comprises the following steps: acquiring state information of the new energy automobile at the current moment, attribute parameters of photovoltaic equipment and attribute parameters of energy storage equipment; processing state information, attribute parameters of photovoltaic equipment and attribute parameters of energy storage equipment based on a target constraint condition and an energy power flexible domain quantitative model by taking resource exchange value maximization of a charging station as a target function, and generating a power flexible domain, a photovoltaic resource flexible domain and an energy storage resource flexible domain; the power flexible domain represents a power adjustment range of charging or discharging of the new energy vehicle, the photovoltaic resource flexible domain represents a power adjustment range of energy supply of the photovoltaic device to the charging station, and the energy storage resource flexible domain represents a power adjustment range of energy supply of the energy storage device to the charging station; and generating a resource scheduling scheme of the charging station according to the power flexible domain, the photovoltaic resource flexible domain and the energy storage resource flexible domain.

The invention discloses a mixed flow safe scheduling method of an electric power voice exchange network, and belongs to the technical field of electric power communication. Comprising the steps of obtaining a to-be-scheduled data stream, and determining a scheduling priority and a security level of the to-be-scheduled data stream according to a service category of the to-be-scheduled data stream; generating a scheduling queue according to the scheduling priority; forwarding a to-be-scheduled data stream to a corresponding scheduling queue, performing encryption processing based on the security level, and completing scheduling under time slot constraint, network control stream traffic constraint and audio stream traffic constraint; and if the service category of the to-be-scheduled data stream is an audio stream, determining the priority of the audio stream and adjusting the sequence of injecting the audio stream into the switch by taking the bandwidth occupancy rate constraint and the delay constraint as a target. The scheduling success rate of the high-bandwidth voice service in the electric power voice switching network is improved, the delay is reduced, and the schedulability of the network is improved; the problem that safety and reliability of existing transmission messages are difficult to achieve at the same time is solved.

The invention provides a multi-objective optimization method, device and equipment based on flexible resources of a charging station, and can be applied to the technical field of new energy automobiles. The method comprises the following steps: in response to a scheduling request for flexible resources of a charging station, obtaining a preset comprehensive resource transaction parameter, a predicted charging amount, a charging duration and historical charging information; obtaining a dynamic resource transaction parameter according to the predicted charging amount and the charging duration; according to the historical charging information, a preset membership function and the fuzzy set, obtaining respective initial discharging willingness of the target object in the N time periods; taking the lowest operation consumption of the charging station and the highest discharge exchange value of the new energy vehicle as target functions, and adjusting the initial resource transaction parameters and the initial discharge intention based on a target constraint condition and a double-layer decision model constructed according to the initial discharge intention and the dynamic resource transaction parameters; and obtaining respective target resource transaction parameters of the N time periods and respective target discharge willingness of the target object in the N time periods.

The invention relates to the technical field of time-space consumption characteristics of cigarettes, and discloses a method, device and equipment for constructing a cigarette time-space consumption characteristic knowledge graph, and the method comprises the steps: obtaining the cigarette consumption data of a to-be-analyzed geographic region; determining a plurality of space-time grids of the to-be-analyzed geographic area by adopting a space-time grid technology and an internet crawler technology based on the cigarette consumption data; aggregating the total sales volume of cigarettes of each specification in each space-time grid based on the cigarette consumption data; constructing a cigarette consumption characteristic knowledge graph model based on the cigarette consumption data; based on the cigarette consumption characteristic knowledge graph model, determining a recessive relation degree between the total sales volume of each standard cigarette and the cigarette consumption characteristics; a pre-calculation pipeline model is constructed based on the recessive relation degree and the cigarette consumption scenes, and the cigarette space-time consumption characteristic knowledge graph under different space-time combination scenes is determined based on the pre-calculation pipeline model, so that the problem that the cigarette sales situation cannot be accurately judged due to lack of relevance between the cigarette business circle and the space-time is solved.

The invention provides a hydrogen electrochemistry and combustion combined power generation device, and belongs to the technical field of chemical power generation, the hydrogen electrochemistry and combustion combined power generation device comprises a gas storage tank, a first exhaust pipe and an exhaust valve mounted on one side of the gas storage tank, and the exhaust valve is mounted on one side of the first exhaust pipe. When the detector displays that the hydrogen content in the gas storage tank is sufficient, the gas storage tank is stopped from being inflated, a switch of the exhaust valve is turned on, and the hydrogen is charged into the gas storage tank under the action of pressure; hydrogen enters the treatment pipeline through the first exhaust pipe, the exhaust valve is controlled to be closed or opened, the gas inlet amount of the hydrogen in the gas storage tank is adjusted, and the exhaust valve can be closed under the standby condition, so that the utilization rate is increased, the consumption of the hydrogen is reduced, and the schedulability of the hydrogen is improved.

An electric vehicle collaborative power grid power supply recovery strategy considering multi-time-domain EV-EPSV comprises the steps that a multi-time-sequence electric vehicle EV equivalent power supply model is established according to a travel rule of an electric vehicle user in a typical travel scene; a load importance index system is constructed, a load power loss model is established in combination with the load capacity and the power failure duration, and quantitative calculation of economic and social losses of different types of loads in the multi-time-sequence power failure process is achieved; and constructing a collaborative optimized power supply model of the electric vehicle EV and the emergency power supply vehicle EPSV, and realizing static power distribution of the electric vehicle and dynamic path and power optimization scheduling of the emergency power supply vehicle. According to the strategy, a multi-time-sequence EV equivalent power supply model is established, a load power loss evaluation system is constructed, and a collaborative optimization scheduling method of the electric vehicle and the emergency power supply vehicle is provided, so that efficient distribution of limited power supply resources and improvement of power supply recovery efficiency are realized. The scheduling flexibility of electric vehicle resources and the power supply toughness of a power grid can be effectively improved in a sudden power failure situation.

This invention discloses an online scalable configuration method and system for dynamically changing time-sensitive networks (TSNs), relating to the Internet of Things (IoT) field. The invention proposes an efficient inter-flow conflict detection mechanism based on multi-level flow grouping. By using a correlation analysis of flow period and offset, it avoids traditional conflict detection methods based on link maximal cliques and link hyperperiods, enabling rapid response to large-scale data flow conflict detection needs. It designs an online incremental method based on decoupling routing and scheduling, as well as offline pre-routing, accelerating the computation speed of routing and scheduling schemes while ensuring scheduling space. This invention reduces the computational complexity of data flow set hyperperiod sensitivity and the maximum link slot occupancy in TSNs, accelerating online scheduling and providing efficient conflict detection support for online scalable configuration.

The application discloses a method for layer scheduling of mixed deep neural network tasks in an embedded real-time system. The method considers the limited CPU and GPU resources of the embedded real-time system, the basic conditions of task scheduling, the time limit of task scheduling and other factors, and designs a scheduling mechanism for layer distribution of mixed deep neural network tasks for the embedded real-time system. The method builds a deep neural network task model and a layer task model, mathematically calculates the task scheduling overhead, the worst response time, the utilization sum of the task under the minimum layer task mapping scheduling, sets an optimization function and a constraint condition solving method, and realizes more balanced utilization of the heterogeneous CPU and GPU computing resources in the real-time system under the condition of shorter worst response time, and improves the real-time performance and schedulability of deep neural network tasks in the embedded real-time system.

The application discloses a kind of cross TSN and PON domain deterministic cooperative routing and scheduling method, by TDM-PON is equivalent to the TSN switch (i.e. OTSN) with TAS function, the cooperative scheduling of OTSN and TSN equipment is realized, the E2E deterministic transmission problem is solved.And the TW constraint formula for OTSN and TSN joint scheduling and the cross-domain cooperative routing and scheduling scheme based on the greedy algorithm are proposed to plan the position of TW, meet the delay, jitter transmission demand of various businesses, compared with traditional non-cooperative scheduling scheme, the schedulability is improved, it is a kind of E2E deterministic transmission capacity, meet the routing and scheduling scheme of the demand of multiple types of businesses.

A multi-time-sequence EV equivalent power supply model modeling method considering a vehicle travel rule comprises the following steps: according to dynamic evolution characteristics of the travel rule of an electric vehicle user, combining travel sample data, dividing into a summer scene and a winter scene according to seasons, and dividing into a workday scene and a holiday scene according to a time sequence to complete data fitting; then, a vehicle travel state transition matrix is established through a semi-Markov chain SMC, and a Monte Carlo method MCM is utilized to perform random simulation on vehicle behaviors to obtain available discharge capacities of the electric vehicles at all moments, so that an electric vehicle cluster in a region is equivalent to a time-varying power supply model, and a multi-time-sequence EV equivalent power supply model is established; according to the method, by introducing a semi-Markov chain and a Monte Carlo random simulation method, available energy storage capacity distribution characteristics of an electric vehicle group in different scenes are described. The model can reflect energy storage laws of residential areas, working areas and shopping areas in different seasons and days, and dynamic quantitative evaluation of the EV available power potential is realized.

The invention relates to a multi-core real-time system task sorting and partition scheduling optimization method based on a DQN, and the method comprises the following steps: obtaining a real-time task set to be scheduled, and extracting the execution time, period, deadline and other feature information of tasks; modeling a task sorting process as a Markov decision process, and performing adaptive optimization on a task processing sequence by using a DQN model to generate a task sorting sequence; on the premise that the structure of a partition scheduling heuristic algorithm is not changed, the task sorting sequence is input into the partition scheduling algorithm, and tasks are distributed; and generating scheduling feedback information according to a task allocation result, wherein the scheduling feedback information is used for updating the DQN model. According to the method, intelligent optimization is carried out on the task sorting stage, the problem that a traditional fixed sorting strategy is difficult to adapt to a complex task set structure is solved, a partition scheduling algorithm can find a feasible task allocation scheme under the high system load condition, and therefore the schedulability and the scheduling success rate of the isomorphic multi-core real-time system are improved.

This invention relates to the field of artificial intelligence and discloses a method and system for scheduling computing resources in heterogeneous computing systems based on intelligent prediction. This solution obtains task description information from a queue of tasks to be scheduled, analyzes task resource requirements, topological constraints, and portability attributes, and classifies tasks into high-resource-demand tasks, ordinary tasks, and elastic tasks. Based on task category, resource fragmentation level, and the arrival status of high-resource-demand tasks, ordinary tasks and elastic tasks are allocated to fragmented partitions, while high-resource-demand tasks are allocated to reserved partitions. Continuous resource blocks matching the resource requirements of high-resource-demand tasks are reserved in the reserved partitions, generating scheduling decisions. Based on the scheduling decisions and the updated reserved and fragmented partitions, task start, pause, migration, and termination operations are executed, and operational feedback information is collected and written to an operational data repository. This improves the schedulability of high-resource-demand tasks.

The present invention discloses a scheduling method for time-triggered flow in a time-sensitive network, which belongs to the field of communication. Two highest priority transmission queues are used to transmit TT flows, and the two transmission queues have different priorities. The TT flow in the highest priority transmission queue is an HT flow, and the flow in the second highest priority transmission queue is an LT flow. It is assumed that the best path in the TT flow path set is the original path, and the remaining paths are redundant paths. Considering the schedulability of the LT flow, combined with the worst response time analysis, it is considered whether the LT flow is converted into the HT flow, and finally a feasible scheduling scheme is found. When the bandwidth capacity is sufficient, the priority of the LT flow that exceeds the deadline can be increased, and the response time of the HT flow can be reduced while ensuring that the LT flow meets the schedulability.

The invention discloses a logistics vehicle comprehensive transportation management method and system, relates to the technical field of logistics vehicle management, solves the technical problem that in the prior art, logistics vehicles cannot be reasonably analyzed according to real-time requirements and supply during transportation, so that the matching efficiency of the logistics vehicles is unqualified, and particularly relates to a transportation real-time matching unit. The main body matching module is used for marking to-be-conveyed vehicles in the logistics conveying network as conveying main bodies, marking the conveying main bodies as conveying performance sets according to conveying parameters of the conveying main bodies, marking goods receiving places of the logistics conveying network as receiving main bodies, marking the receiving main bodies as receiving demand interval sets according to receiving parameters of the receiving main bodies, and carrying out main body matching; after transportation real-time matching is completed, a network efficiency evaluation time period is set; it is guaranteed that the vehicle supply performance in the transportation process can meet the requirement of an actual goods receiving place, and the supply efficiency of logistics vehicle transportation is improved.