9 results about "Multilevel queue" patented technology

The invention provides a computing node memory management method and device, equipment and a program product, and relates to the technical field of computers. The method comprises the steps that a multi-stage queue system is constructed, the multi-stage queue system comprises queue units of at least two stages of storage media, and the queue units are arranged in a layered mode according to the performance of the storage media; calculating a capacity threshold value of each level of queue unit in the multi-level queue system based on an available memory of a calculation node and a preset proportionality coefficient; and dynamically adjusting the capacity of each level of queue unit according to the capacity threshold, and migrating the data to the low-priority queue unit when the high-priority queue unit is fully loaded. The method is used for achieving the effect of improving the memory resource utilization rate.

The application relates to the field of industrial internet and intelligent data processing, and discloses a cross-domain low-latency transmission device data communication method, system, medium and equipment based on semantic perception, which comprises the following steps: collecting multi-source heterogeneous original data of a comprehensive transmission device to obtain original message data sets, and obtaining semantic entities through message analysis and semantic coding; compressing and multi-stage queue scheduling the semantic entities according to link states and service priority labels to obtain edge uplink compressed semantic streams; decompressing the edge uplink compressed semantic streams, and deserializing the decompressed binary streams into standard semantic data object sets; analyzing the attribute types of each data in the standard semantic data object sets, dynamically distributing the computing tasks corresponding to the attribute types to a heterogeneous computing cluster based on preset computing affinity rules, and outputting a result data stream; and a control engine dynamically generating display control instructions according to the result data stream, and an application domain adaptively rendering and visually displaying according to the display control instructions.

The invention relates to the technical field of computer data storage system performance evaluation, and discloses a cloud object storage persistent layer tail delay modeling method based on a queue model, which comprises the following steps: establishing a logic processing flow of a cloud object storage persistent layer, and defining a request transfer relationship to construct a multi-stage queue model; load modulation is executed based on the collected characteristic parameters, and a simulation request load containing sudden overload and hotspot inclination is generated; executing a fault disturbance mechanism in the multi-level queue model, establishing a server-level and queue-level double-layer fault system, generating a fault disturbance factor, and correcting the actual processing time of the request by using a maximum value strategy; and tracking the queuing waiting time of the request full link and the corrected service time, accumulating to obtain time delay distribution, and extracting a tail time delay index. According to the method, the problem of long tail phenomenon prediction distortion of a traditional model is effectively solved by simulating an unsteady load and a transient fault, and the accuracy of system performance evaluation under a complex working condition is improved.

The application relates to the technical field of medical informatization, and discloses a case consultation and pathological section automatic diagnosis system based on AI analysis, which comprises a server and a client; the server generates a lesion binary mask by using a deep learning model, constructs a sparse quadtree index carrying a composite state bit mask, and generates a vector section with the background removed; the client splits resource requests to a multistage queue according to the bit mask in the index, and preferentially loads high-value area images; a three-layer heterogeneous rendering architecture is adopted, and adaptive Gaussian blur is used to process the vector section to realize smooth transition; meanwhile, a behavior audit module collects viewport tracks based on a physical rendering completion state, calculates high-risk area reading coverage, and controls diagnosis report submission permission. The application effectively reduces the transmission bandwidth of gigapixel-level sections, improves loading flicker, and reduces the risk of missed diagnosis caused by network delay or human negligence.

This application relates to the field of medical information technology and discloses an AI-based automatic diagnosis system for case consultation and pathological slides. The system includes a server and a client. The server uses a deep learning model to generate a binary mask of lesions, constructs a sparse quadtree index carrying a composite state bitmask, and generates vector slices with background removed. The client distributes resource requests to multi-level queues based on the bitmask in the index, prioritizing the loading of high-value region images. A three-layer heterogeneous rendering architecture is adopted, using adaptive Gaussian blur to process the vector slices for smooth transitions. Simultaneously, a behavior auditing module completes the state acquisition viewport trajectory based on physical rendering, calculates the coverage rate of high-risk areas, and controls the submission permissions of diagnostic reports. This invention effectively reduces the transmission bandwidth of gigapixel-level slices, improves loading flicker, and reduces the risk of missed diagnoses due to network latency or human error.

The present application relates to a kind of flow optimization method and system for ship-shore offline data transmission under restricted network, the system first real-time acquisition ship end including the multi-source data of safety alarm data, collision avoidance trajectory data and sensor data, and carry out differentiated preprocessing to generate lightweight data set, then real-time compression processing is carried out;Then construct the priority layered model including multi-stage queue scheduling mechanism, transmission scheduling is implemented according to safety level, and flow quota monitoring and fuse protection are carried out, and network state is monitored in real time, when network state is abnormal, suspend the data transmission of all queues, and record the transmission breakpoint state of each queue and the data packet sequence number of uncompleted transmission when abnormal occurs, finally, after network state recovers, based on the transmission breakpoint state of each queue and the offline data packet sequence number of uncompleted transmission recorded, data is continued according to priority order, and the data integrity is verified using hash check method, complete the flow optimization of ship-shore offline data transmission.

The present application belongs to the technical field of storage system scheduling optimization, and discloses a storage system, a multi-queue RTC coroutine scheduling method and a scheduler. The scheduling method comprises the following steps: moving a read task to a main queue; selecting tasks in a multi-level queue as ready tasks one by one, and the high-priority queue in the multi-level queue is prior to the main queue; regularly polling a storage device to call a target data block of a suspended task and then placing the suspended task in a high-priority queue; if the selected task is from the main queue, analyzing query information and searching in a cache; if the target data block is not found in the cache, constructing a storage device read request to enable the storage device to query internally and suspend the current ready task, and then executing the next ready task; if the current selected ready task is from the high-priority queue, performing a request response, and then executing the next ready task. The above scheduling method can reduce the tail latency of the storage system when performing RTC coroutine scheduling in the same execution domain.

This application discloses a method and system for segmented resource reclamation in large-scale network scenarios of a network range, relating to network ranges and cyberspace security technologies. The method includes: parsing the resource topology of the target network scenario to generate a resource list and individual dependency relationships in a specified format; formulating a segmented, structured reclamation plan based on the parsing results; breaking down the reclamation plan into independent reclamation tasks and placing these tasks into multi-level queues; dynamically generating queues based on the task types of the reclamation tasks, sorting the queues according to the weight of the task types, with higher-weighted tasks having higher priority; and executing reclamation operations according to priority. This application effectively solves the problems of excessively long reclamation processes and low resource reuse rates in existing technologies, significantly improving resource reclamation efficiency and accelerating the turnover speed of range resources.

The application relates to a phased array radar task scheduling method, which comprises the following steps: a task registration center sends a radar task scheduling request to a scheduling module; a comprehensive priority calculation module calculates a current task comprehensive priority and generates radar task priority data; and a scheduling decision module sends the radar task to a multi-level priority queue according to the initial comprehensive priority of the corresponding radar task and inserts the radar task into the tail of the corresponding priority queue. The application has the beneficial effect that the dynamic scheduling process in the application adopts a scheduling program combining sequential execution and parallel execution, which can realize timely scheduling of phased array radar tasks, meet the scheduling priority demand of high-priority phased array radar tasks, and improve the overall scheduling efficiency of the radar scheduling system through the dynamic scheduling mode of the multi-level queue.