24 results about "Priority function" patented technology

The invention discloses a multi-agent asynchronous collaboration method and system under a centralized architecture, and is suitable for task scheduling and resource coordination of heterogeneous agents in an intelligent Internet of Things environment. According to the method, a main scheduling node centralized control mechanism is adopted, a scheduling priority function is calculated in combination with a multi-agent game model according to real-time state information of a plurality of heterogeneous agents, and asynchronous allocation and feedback control of tasks are achieved. Meanwhile, an asynchronous and synchronous window mechanism is introduced, the scheduling continuity and efficiency can still be kept under the condition of incomplete information, and fairness and robustness of task distribution are achieved through agent feedback delay modeling and revenue function design. The system throughput and the response speed are improved, the isomerism and the expandability are considered, and the method is suitable for multi-agent cooperation scenes such as disaster emergency, industrial manufacturing, smart home and intelligent traffic.

The invention discloses a central control system of a digital multimedia exhibition hall, and relates to the technical field of central control systems, a time-space correlation analysis module constructs a fault propagation probability graph of exhibition hall equipment and audience behaviors, and a coupling degree analysis module verifies a cross-layer causal relationship by using a directional disturbance injection method based on the fault propagation probability graph; a coupling degree index is fitted by analyzing an incidence matrix of a resource scheduling operation and a fault propagation chain, a dynamic priority decision module fits a fault propagation cost gradient, the coupling degree index and the fault propagation cost gradient are input into a priority function, and a collaborative priority of a short-term suppression action and a long-term eradication action is calculated. And the strategy execution module calls the execution sequences of the two types of actions through the interface of the central control platform, and executes corresponding strategy actions. The central control system is not only high in dynamic adaptive capacity, but also capable of responding to audience demands and equipment state changes in real time, improving exhibition management efficiency and enhancing immersive experience of audiences.

The invention discloses a value maximization calculation unloading method and system based on dynamic priority scheduling and reinforcement learning. According to the method, an optimization framework with maximization of the'long-term average task value 'of the system as a core target is constructed, and the task value is defined by introducing an exponential value function, so that the problem of'value neglect' existing in a traditional'average delay or energy consumption optimization 'strategy is solved. According to the value-oriented optimization mechanism, the multi-agent system can autonomously learn a strategy which preferentially ensures high-value task execution when resources are limited in the training process. In addition, the invention further provides a dynamic priority function which can accurately measure the actual emergency level of the task at different moments, so that the defect that a static priority model is difficult to deal with the real-time change of the emergency state is overcome. Particularly, the dynamic priority method is not only used for task selection in a waiting queue, but also innovatively introduced into a resource dynamic allocation mechanism of a running queue.

The invention discloses a process manufacturing factory material scheduling system based on intelligent agents. The system comprises a central coordination intelligent agent, a plurality of process intelligent agents, a video subsystem and a trolley subsystem. The scheduling method based on the system comprises the following steps: S1, initializing the system; s2, real-time state monitoring; s3, dolly scheduling decision making: the process agent calculates the priority of each transport task according to the priority function; s4, according to the queue capacity of the downstream workshop and the current outlet material quantity, S5, path decision making; s6, returning a path decision; s7, performing global optimization adjustment; s8, performing exception handling and cooperative adjustment; according to the invention, organic combination of local autonomy and global coordination is realized, the real-time response capability of the system is ensured, global optimization is also realized, and compared with traditional centralized control, the system is faster in response; multiple constraint conditions and optimization targets are considered, the average waiting time of materials can be reduced by one third, and the trolley utilization rate can be improved by one fourth.

The invention provides an electric power operation and maintenance big data monitoring method, and relates to the technical field of data monitoring management, and the method comprises the steps: carrying out the joint expression of electric power data through a multi-dimensional data fusion scheme; embedding the physical topological relation and the power flow information of the power system into the graph neural network structure, and identifying spatial correlation and load imbalance characteristics between devices; embedding a neighborhood load change guide item and a periodic stability constraint based on a gated loop network, and carrying out precise modeling on an equipment state change rule on different time scales; the prediction probability and the state offset score are fused, a hierarchical early warning mechanism oriented to regional risks is established, a scheduling priority function is constructed in combination with the importance weight and the operation load of the equipment, and intelligent scheduling for different equipment is realized; through dynamic load fluctuation analysis and scheduling optimization of the computing nodes, high-risk equipment preferentially obtains computing resource support, and it is ensured that the system has efficient response and computing capacity in a high-concurrency and high-load power grid environment.

The invention discloses a multi-target radar time resource allocation method based on dynamic priority evaluation, and the method comprises the steps: building a target motion model and a radar observation model, and enabling the target motion model to describe a target motion process through employing a plurality of models containing a possible motion mode; obtaining a radar measurement value according to the target motion model and the radar observation model, and constructing a priority function including target motion characteristics, spatial positions and historical observation information; embedding the priority function into a Bayesian Cramer-Rao lower bound, and establishing a radar resource allocation model which takes a minimum weighted Bayesian Cramer-Rao lower bound sum as a target function, takes a dwell time sum not exceeding a resource scheduling period and takes each target dwell time meeting a constraint range as conditions; and solving the radar resource allocation model through a gradient projection method to obtain a resource allocation result. Reasonable configuration of radar residence time resources can be realized, and the target tracking precision and reliability are effectively improved.

In described examples, a method of operating a system on chip (SoC) includes the following steps. Receive a component description that describes connections among circuit elements of the SoC. Receive a list of specified peripherals to activate while the SoC boots. Receive a hint data that describes a bypass mode clock frequency of a phase locked loop (PLL) of the SoC. Determine an operational data in response to the component description, the hint data, and the specified peripherals. The operational data specifies a subset of the circuit elements required to make the specified peripherals operational. Initialize, and limit the initializing, to the subset of the circuit elements.

The invention discloses a resource dynamic scheduling method and system for a computing network cluster in a power grid information system, and the method comprises the steps: enabling a data collection module to collect the historical resource use data of each task in the cluster in real time after a user submits a computing network calculation task through a task submission interface; the resource demand prediction module extracts spatial and temporal features by means of a one-dimensional convolution and stacking LSTM structure, and accurately predicts task resource demands in a plurality of time slots in the future; the multi-level queue scheduling decision module constructs a comprehensive priority function according to task waiting time, predicted resource requirements and user-defined priorities, realizes task layering and dynamic sorting, and generates a resource allocation scheme with the lowest total scheduling cost based on a rolling horizon optimization method; and the task migration module converts the scheduling decision into actual operation, and completes containerization deployment and resource dynamic adjustment through a Kubernetes interface, thereby realizing real-time optimization and efficient utilization of computing network cluster resources. According to the method, the computing network cluster resource scheduling efficiency can be improved to the greatest extent.

The invention relates to the technical field of software security, and provides a source code high-order vulnerability detection method and system based on execution path analysis, and the method comprises the steps: obtaining a to-be-analyzed source code, and constructing a function execution graph for each function in the source code; on the function execution graph, starting from a function entry node, generating a function execution path according to an execution sequence edge, and applying bounded constraint to a function execution path generation process; calculating a priority score for any function execution path by adopting a path priority function, and sorting and screening the function execution paths based on the priority scores to obtain candidate execution paths; and obtaining a vulnerability type and vulnerability location through a path coding model, a statement encoder, an attention mechanism and a vulnerability detection model. And the vulnerability detection accuracy and the positioning interpretability are improved under the controllable calculation overhead.

The application relates to a V2G-VPP resource aggregation method fusing isomerism and dynamic priority, which comprises the following steps: through protocol non-inductive access, communication data of various types of devices involved in V2G-VPP are processed, automatic analysis and conversion of cross-protocol device instructions are realized; based on the analyzed and converted data, line chain characteristics and social attributes are extracted, a user elastic quantization model is constructed, and an elastic coefficient is calculated; a dynamic coupling priority function driven by power grid demand and resource state is constructed by combining power grid frequency modulation demand, predicted moving probability and the elastic coefficient, resource priority is dynamically evaluated, each electric vehicle is sorted and screened in descending order based on the resource priority, a resource priority scheduling sequence is generated and optimized. Compared with the prior art, the application has the advantages of being capable of improving protocol conversion efficiency, improving high-value resource identification accuracy, greatly reducing V2G schedulable capacity evaluation error and the like.

The invention provides a time delay sensitive service scheduling method and device and a readable storage medium, and the method comprises the steps: obtaining a network parameter of a user, and a time delay requirement parameter corresponding to the user; determining a baseline enhancement proportional fair (EPF) priority based on the network parameters; determining a priority adjustment factor based on the time delay requirement parameter; determining an EPF priority based on a time delay requirement according to the baseline EPF priority and the priority adjustment factor; and performing user resource allocation according to the determined EPF priority. According to the method and the device, the multi-dimensional optimization target and the priority function are introduced, so that the time delay requirements of different services are accurately met, and the flexibility and the fairness of network scheduling are improved. Besides, by providing configuration options of the time delay factors, flexible adjustment of the algorithm among the rate, the resource fairness and the time delay dimension is realized, and the applicability and the flexibility of the algorithm are further improved.

The present application belongs to the technical field of computer, the present application discloses a heterogeneous SoC task adaptive allocation system;Including: migration determination module, identifies the load imbalance state between each core in the running process, and filters migration candidate task;Migration preparation module, the running state of migration candidate task generates baseline snapshot, and carries out hierarchical division;Semantic annotation module, the memory page occupied by migration candidate task is carried out semantic annotation, and the regularized semantic score is calculated, and the memory page data is allocated level according to regularized semantic score;Compression migration module, the layered state data is carried out mixed compression and adaptive restoration in the migration process;Preheat loading module, on the target core after migration, according to the preheat priority sequence, the preheat loading of the code and data of high priority function is executed to the cache of target core;Through semantic annotation and hierarchical compression, the transmission data volume is effectively reduced, and the migration pause time is reduced.

To provide a method and a system for constructing a customized knowledge graph.SOLUTION: The method generates a seed graph by extracting entities and relationships between the entities from the user dataset based on a specific rule. The large knowledge graph is then traversed using a finite state machine to identify candidate entities and / or relationships to add to the seed graph. A priority function is used to select entities and / or relations from the candidate entities and / or relations. The selected entities and / or relationships are then added to the seed graph to generate the customized knowledge graph.SELECTED DRAWING: Figure 5

The invention relates to a multi-sensor adaptive task scheduling method for hoisting operation monitoring, and belongs to the technical field of multi-sensor task scheduling. The method comprises the steps of establishing a task scheduling basic model; calculating scheduling time delay and communication bandwidth occupation parameters; calculating a system data communication rate supporting scheduling of all the sensors at the minimum frequency under the condition that the communication bandwidth is insufficient; establishing a multi-sensor task optimization function; and the gateway automatically generates an optimal scheduling scheme of each sensor task. According to the method, the task priority function is constructed, so that the task processing sequence of each sensor can be adaptively decided, and the method has relatively good environmental adaptability to the real-time change of a hoisting operation scene state; the relation between the task execution efficiency of the safety monitoring multi-sensor in the hoisting operation scene and the limited bandwidth resource of the system network is effectively balanced, and the optimal execution of the safety monitoring task under the limited resource condition is ensured.

The invention relates to the technical field of video conference operation and maintenance management, in particular to an automatic operation and maintenance management method and system for a video conference, and the method comprises the steps: obtaining current conference task data, constructing a state matrix based on the conference task data, calculating a state score vector and abnormal strength of each terminal, and comparing the state score vector and abnormal strength with a preset threshold value; generating a terminal exception indication vector according to a comparison result, generating a to-be-executed set according to the terminal exception indication vector, dividing the to-be-executed set into corresponding action queues according to recommended action types, generating an execution sequence of each terminal based on a priority function, and executing the recommended action types based on the execution sequence, generating an execution result vector and an execution record set; the terminal sets which are not successfully confirmed after the automatic operation and maintenance are finished are reserved, priority weights are constructed for the terminal sets which are not successfully confirmed, the terminal sets are sorted, and prompt information is generated for the terminal sets in combination with the sorting result and the execution record set.

The invention relates to a V2G-VPP resource aggregation method fusing isomerism and dynamic priority, and the method comprises the following steps: carrying out the processing of communication data of various types of equipment involved in V2G-VPP through protocol non-sensitive access, and achieving the automatic analysis and conversion of a cross-protocol equipment instruction; extracting trip chain features and social attributes based on the analyzed and converted data, constructing a user elasticity quantitative model, and calculating an elasticity coefficient; and constructing a dynamic coupling priority function of power grid demand and resource state dual drive by combining the power grid frequency modulation demand, the predicted movement probability and the elastic coefficient, dynamically evaluating the resource priority, performing descending sorting and screening based on the resource priority of each electric vehicle, generating a resource priority scheduling sequence, and performing optimization. Compared with the prior art, the method has the advantages that the protocol conversion efficiency can be improved, the high-value resource identification accuracy is improved, and the V2G schedulable capacity evaluation error is greatly reduced.

The application discloses a delay-tolerant network cache management method, system, device, medium and program product based on connected block sociality, and belongs to the technical field of wireless communication and computer network. The management method comprises the following steps: each node in the delay-tolerant network constructs a connected block society with a limited hop number centering on the node itself, extracts the social characteristics of the node and the business message in the connected block through a consensus message broadcast and multi-hop propagation mechanism, and performs real-time synchronous updating; based on the social characteristics, a multi-dimensional cache priority function is constructed by fusing the properties of the business message, the cache value of each business message is quantified comprehensively, and low-value business messages are discarded when the cache is full; the system, the device and the medium are used for realizing the mechanism; the program product comprises a computer program for realizing the mechanism; and accurate business message screening when the cache is full is realized, so that the arrival rate of the business message is improved.

The application relates to the technical field of software security, and provides a source code high-order vulnerability detection method and system based on execution path analysis, which comprises the following steps: obtaining source code to be analyzed, and constructing a function execution graph for each function in the source code; starting from a function entry node on the function execution graph, generating a function execution path according to an execution order edge, and imposing a bounded constraint on the function execution path generation process; calculating a priority score for any function execution path by using a path priority function, sorting and screening the function execution paths based on the priority score, and obtaining a candidate execution path; and obtaining a vulnerability type and vulnerability positioning by using a path encoding model, a statement encoder, an attention mechanism and a vulnerability detection model. The vulnerability detection accuracy and positioning interpretability are improved under controllable computing overhead.

A computer-implemented method of scheduling jobs of a manufacturing or logistics process using a priority function. The priority function is evaluated on multiple to be scheduled jobs to obtain multiple respective priority values. The priority function is defined to invoke a kernel function. The kernel function is defined to compare representations of two respective jobs. Evaluating the priority function on a selected job comprises evaluating the kernel function on representations of the selected job and one or more reference jobs. The schedule for the multiple jobs is determined based on the priority values and is then output to enable the multiple jobs to be carried out according to the schedule.

This invention discloses a value maximization computational offloading method and system based on dynamic priority scheduling and reinforcement learning. The invention constructs an optimization framework with the core objective of maximizing the system's "long-term average task value." By introducing an exponential value function to define task value, it addresses the "value neglect" problem inherent in traditional "average latency or energy consumption optimization" strategies. This value-oriented optimization mechanism enables multi-agent systems to autonomously learn strategies to prioritize high-value tasks during training when resources are limited. Furthermore, this invention proposes a dynamic priority function that accurately measures the actual urgency level of tasks at different times, thus overcoming the shortcomings of "static priority" models in handling real-time changes in urgency. Notably, this dynamic priority method is not only used for task selection in the waiting queue but is also innovatively introduced into the dynamic resource allocation mechanism of the running queue.

The application discloses a digital multimedia exhibition hall central control system, and relates to the technical field of central control systems.The time-space correlation analysis module constructs a fault propagation probability atlas of exhibition hall equipment and audience behavior, the coupling degree analysis module verifies cross-layer causal relationships using a directional disturbance injection method based on the fault propagation probability atlas, the correlation matrix of resource scheduling operations and fault propagation chains is analyzed, the coupling degree index is fitted, the dynamic priority decision module fits the fault propagation cost gradient, the coupling degree index and the fault propagation cost gradient are input into the priority function, the synergistic priority of short-term suppression actions and long-term eradication actions is calculated, the execution sequence of the two types of actions is obtained, and the strategy execution module calls the execution sequence of the two types of actions through the central control platform interface and executes corresponding strategy actions.The central control system not only has strong dynamic adaptability, but also can respond to audience needs and equipment state changes in real time, improve exhibition management efficiency and enhance the immersive experience of audiences.

To provide a communication system, a program, and a communication method capable of securing reliability of radio communication even if a communication environment is changed.SOLUTION: A communication system 100 comprises: a route information acquisition section 104 for acquiring route information; a congestion area prediction section 106 for predicting a congestion area A; an arrival time prediction section 107 for predicting arrival time of one's own vehicle 81 at the congestion area A; a communication load prediction section 108 for predicting a communication load occurring in the congestion area A at arrival time; and a communication setting section 110 that sets a total data amount of data required for executing multiple functions to a total data amount for preventing congestion from occurring in the congestion area A at schedule arrival time, and performs setting so as to transmit and receive data required for executing a high-priority function by utilizing multiple radio lines.SELECTED DRAWING: Figure 6

The application discloses an RTC transmission optimization technology based on Priority NACK, which comprises the following steps: firstly, a data structure PrioritySeqBuffer with a packet sequence number as a storage object is designed at a receiving end; secondly, a NACK request protocol with a priority function is designed at the receiving end; and finally, a protocol processing module PriorityNackModule is designed. According to the application, all lost data packets have the same priority by using a general NACK request protocol realized internally in WebRTC, and the rule is to perform packet loss retransmission once upon receiving a NACK request. Compared with the general NACK request protocol realized internally in WebRTC, the Priority NACK request can greatly increase the packet integrity and continuity in the scenario that only one lost data packet needs to be supplemented to make a large number of subsequent data packets continuous, thereby improving the decoding and rendering frame rate of a video frame and reducing lag.

The invention provides a precast beam intelligent production management and control method and system, and the method comprises the steps: uniformly extracting key parameters through BIM and a drawing, constructing a six-dimensional structure vector, and converting the conventional experience cognition into numerical input; secondly, a tensioning and maintenance double-risk function is established in combination with the field temperature and humidity environment and the equipment capacity, and the process feasibility is quantified in advance; a comprehensive priority function is designed based on the structure-process vector and the risk score, a conflict-free production scheduling sequence is generated by means of a resource occupation matrix sliding window algorithm, and the construction period pressure and the resource bottleneck are considered; and finally, the scheduling result is mapped and packaged into an execution instruction packet which can be directly called by the MES and the mobile terminal according to a uniform field, and automatic plan issuing and team one-key material getting operation are realized. According to the method, the structure complexity, the process difficulty and the resource capacity are cyclically iterated in the same data chain, the stubborn problems of'arrangement, inaccurate arrangement and unreal falling 'of a traditional beam field are thoroughly solved, and the method has important engineering value for guaranteeing delivery nodes of urgent projects of bridge engineering.