2830 results about "Throughput" patented technology

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention discloses an SDR-oriented heterogeneous task scheduling and transmission system and method.The system comprises a heterogeneous platform composed of an ARM and an FPGA, a dynamic scheduling decision engine is arranged in the ARM and receives a task feature vector and a platform state parameter set as input, and the dynamic scheduling decision engine evaluates income and cost of task allocation to the ARM or the FPGA for execution to make a task allocation decision; the data generated in the task execution process or the execution completion result are interacted and synchronized between the ARM and the FPGA through the heterogeneous shared memory area HSM. The method realizes reduction of signal processing pipeline delay, improvement of data throughput and optimization of system power consumption, and is suitable for communication, electronic countermeasure and other scenes needing high-performance real-time signal processing.

The present invention relates to adaptive multi-state encoding and processing in virtualized computing environments. The system includes a virtualized state selection module that dynamically transitions between binary, ternary, quaternary, and higher-order encoding states based on workload, bandwidth, security posture, and compliance requirements. A virtual encoding engine utilizes hardware-accelerated components, such as vFPGAs, vGPUs, and cTPUs, to enhance encoding throughput. A compliance-driven feedback controller continuously monitors encoding efficiency, threat levels, and adherence to mandates such as GDPR, HIPAA, and FIPS 140-3. Additional features include AI-based anomaly detection, federated model refinement, distributed ledger-backed audit trails, and quantum-resistant encoding techniques. By integrating intelligent encoding decisions with scalable compliance enforcement, the system enables high-performance, secure, and regulation-ready data processing across distributed cloud and edge environments, delivering measurable improvements in system responsiveness, data integrity, and operational trust.

The invention relates to the technical field of storage optimization, in particular to a data storage management system and method based on big data, and the system comprises an efficiency monitoring module, a medium evaluation module, a decision generation module, a strategy execution module and an effect feedback module. According to the method, input and output delay, a medium health attenuation curve and a bandwidth fluctuation map are collected, a dynamic feature space is constructed, the stability of the system is judged in combination with a Lyapunov index, early warning of performance abnormity is achieved, health indexes such as an erasing cycle, a seeking error rate and a charge retention rate are fused, and the access frequency and the load pressure are combined. Calculating a compensation coefficient, improving heterogeneous medium cooperation efficiency, pre-migration evaluation cost and topology adjustment income, improving resource configuration accuracy, dynamically adjusting hot and cold data distribution and node load, enhancing system scheduling flexibility, comparing throughput, medium wear rate and synchronous delay, and quantifying stability gain brought by configuration change; and a closed-loop process from identification to feedback is constructed.

A low earth orbit satellite (LEO)-based communication system and method. The system comprises a low-orbit satellite constellation, a ground station, a user terminal and a network management and control center. The network management and control center comprises a track and link prediction module, a link quality prediction and evaluation module, a predictive resource pre-allocation and dynamic adjustment module and a space-ground cooperative control module. According to the communication system, active resource optimization and dynamic adjustment before actual switching are realized through track and link prediction, link quality prediction, predictive resource pre-allocation and dynamic adjustment and air-ground cooperative control. According to the method, the switching time delay can be greatly reduced, the packet loss rate is remarkably reduced, and the system throughput and the access success rate are also remarkably improved. Through predictive management, the performance, reliability and user experience of LEO communication are comprehensively improved.

The invention provides an end-side multi-modal large model accelerated reasoning method and system, and the method comprises the steps: carrying out the two-stage screening and rearrangement of visual tokens based on the CLS attention and text-to-visual attention in a visual encoder and pre-filling stage, and constructing a sparse attention and sparse key value cache; in a decoding stage, an important neuron set is judged according to activation gating or historical statistics, only a corresponding feedforward network weight is pulled and calculated, missed weights are loaded on demand through asynchronous I / O, and hot neurons are maintained in a high-speed memory to utilize model sparsity, so that video memory / memory occupancy and calculation overhead are remarkably reduced on an end side; throughput and time delay performance are improved. According to the method, the internal memory and computing resources required by reasoning of the multi-modal large language model are reduced from two dimensions by utilizing the endogenous sparsity of the end-side large language model in input and the model, so that a higher reasoning speed is achieved by utilizing fewer resources on the premise of keeping the size of the model unchanged, and the performance of the whole system is improved.

The present invention relates to a system and computer implemented method for cost-aware autoscaling of artificial intelligence workloads using predictive queueing models, designed to achieve proactive and economically optimized scaling of computational resources across cloud and edge environments. The invention introduces a predictive queueing-based technique that anticipates future workload congestion by modeling dynamic task arrivals and service times using a stochastic queueing process. A cost estimation unit computes the total projected operational cost of potential scaling actions by integrating real-time infrastructure pricing data, predicted delay penalties derived from service-level objectives, and estimated energy consumption. A scaling decision unit applies reinforcement learning-based optimization to select the scaling action that minimizes total cost while ensuring compliance with latency and throughput constraints. The system includes a hardware-integrated autoscaling controller device comprising a predictive computation processor, cost-decision processor, and scaling actuation interface configured for real-time execution of predictive and scaling operations.

The invention relates to the technical field of communication, in particular to a dynamic priority dual-mode communication fault-tolerant switching method and equipment, and aims to solve the problems of communication interruption and service quality reduction when a main link is degraded in a complex electromagnetic environment. According to the method, a dynamic priority evaluation model and a dual-mode protocol cooperation mechanism are constructed, throughput, bit error rate, delay jitter, task semantic priority and aging constraint data of a main link and a standby link are collected in real time, a priority weight and a communication mode decision matrix are generated through combined modeling, an atomization switching instruction is executed according to the priority weight and the communication mode decision matrix, and a communication mode is switched. And completing path reconstruction and resource redistribution. The system further introduces link health prediction, a resource reservation pool and an online learning feedback loop, and supports fault pre-judgment, key task preemption and model adaptive optimization. According to the method, the limitation of a static threshold is broken through, the task semantics and channel state joint scheduling is realized, and the communication reliability, the service continuity and the intelligent fault-tolerant capability of a distributed system in a high-risk scene are remarkably improved.

The invention relates to the technical field of cooperative control, in particular to a full-intelligent simulation load distributed cooperative control method, which comprises the following steps of: acquiring node load data, dynamically predicting, adjusting and distributing, performing cooperative scheduling optimization control, and generating an intelligent load control scheme. According to the method, the load state information is extracted in real time and standardized verification is carried out, so that the running state of each node has a unified measurement basis, task allocation is carried out in combination with node processing capacity and throughput performance, and task scheduling and node performance dynamic matching are realized; prejudgment type load regulation and control are achieved by combining historical data trend prediction with a current state, the risk of task delay and node overload is avoided, a task allocation strategy is continuously optimized by utilizing real-time state feedback, the resource use efficiency is improved, the cooperative relation between nodes is strengthened, and the sensitivity and consistency of system scheduling are guaranteed in a dynamic load change environment. And the full-process adaptivity and collaborative stability of task scheduling in a multi-node system are supported.

The present invention provides systems and methods for cellular network performance monitoring and optimization, enabling SIM-based devices to dynamically adapt to changing network conditions for improved connectivity. The invention introduces a process that includes determining baseline path performance through detailed probing of network metrics, continuously assessing current path performance via real-time monitoring, and instructing the SIM to switch from its current connected mobile network carrier to an alternate carrier when predefined performance thresholds are not met. Switching instructions are securely delivered Over-The-Air (OTA) to the SIM, ensuring seamless transitions to the most efficient and reliable network path. The system leverages both active and passive application layer observations to optimize latency, throughput, and reliability while supporting diverse applications, including IoT devices, industrial systems, and consumer devices.

The invention belongs to the technical field of data transmission and processing, and discloses a zero-copy-based multi-thread high-throughput data stream channel separation method and system, and the method comprises the steps: directly writing a mixed data stream into a front-end buffer region configured as an annular structure through a data receiving module by adopting direct memory access; then, a multi-thread processing module dynamically allocates a plurality of processing threads from a thread pool to separate channel data in parallel, each thread adopts a zero copy algorithm based on pointer offset, positions the channel data in a memory, creates pointer reference and associates the channel data to a corresponding rear-end buffer area, and logic separation is achieved without physical copy; and finally, the data storage module efficiently writes the separated data into persistent storage in an asynchronous I / O mode. According to the method, zero-copy, multi-thread parallel and two-stage dynamic buffering strategies are combined, the data separation efficiency is remarkably improved, CPU occupation and memory bandwidth are greatly reduced, and the real-time performance and stability of high-throughput data processing are guaranteed.

The invention discloses an asynchronous parallel reasoning method, system and equipment for a hybrid expert model and a medium, which are corresponding schemes: decoupling synchronization of calculation and communication between GPUs (Graphics Processing Unit) caused by all-to-all set communication in expert parallelism, allowing asynchronous parallelism of model calculation and lexical metadata communication, and solving the problem of asynchronous parallelism of the model calculation and lexical metadata communication. Data communication overhead caused by expert parallelization is fully masked, and synchronization waiting overhead is eliminated; aiming at the phenomenon of uneven cold and heat of experts in reasoning, the hot experts are preferentially placed in the GPU, the cold experts are laterally loaded in the CPU so as to release the video memory space of the GPU, and the calculation efficiency of the GPU can be improved by increasing the batch size during reasoning; efficient resource scheduling is realized by dynamically selecting a computing unit which is most suitable for execution and a cold expert which needs to be loaded; generally speaking, the communication overhead and the waiting overhead during parallel reasoning of experts can be remarkably reduced, meanwhile, the calculation efficiency of the GPU is improved, and the overall throughput performance in the reasoning process is optimized.

The invention provides a DCU-based high-performance sparse stiffness matrix vector multiplication method, which comprises the following steps of: according to a sparse stiffness matrix, dividing a non-zero element into a plurality of calculation unit blocks by rows, pre-loading non-zero element data to an L1 shared memory or a register file through an on-chip shared memory controller of the DCU, a high-bandwidth crossbar switch of the DCU is used for realizing data copying and transmission; starting multi-row fusion execution for a short row of which the row non-zero element is lower than a DCU single-instruction multi-data width threshold value; constructing a wavefront scheduler based on a DCU asynchronous computing engine: binding an independent instruction cache region for each wavefront, and loading a multiply-add operation instruction set in advance through a prefetch instruction queue; a calculation unit state register is established, and when a wavefront scheduler ready signal is triggered, a scalar unit of the DCU is activated to execute calculation; and realizing cross-thread block reduction by adopting a DCU atomic operation accelerator. According to the method, the calculation throughput and the memory bandwidth utilization rate of large-scale structural mechanics stiffness matrix vector multiplication are effectively improved.

The invention relates to the technical field of data flow control, in particular to a heterogeneous data flow synchronous control method and system.The method comprises the following steps that multi-source data segment receiving time is obtained, a time interval sequence is constructed, an interval difference value and a synchronous difference quantity are calculated, the sensitivity level is judged, a channel throughput change is extracted, a descending section is marked, and a priority index is generated; and mapping the high-sensitivity stream to a first-stage or second-stage output path to sort and distribute key stream segments, judging that serial numbers are continuously combined or independently output, and generating intersection and sequence information. According to the method, the time interval sequence is constructed based on the receiving time of the multiple data sources, the interval deviation is quantified, the labels are divided through the synchronization difference quantity, the sensitive level structure is established, differential management of the synchronization precision requirement of the heterogeneous data streams is achieved, the response efficiency and the alignment precision of the high-sensitivity data segments are improved in the data distribution process, and the data distribution efficiency is improved. And meanwhile, the competition of a low-sensitivity section for output resources is reduced, and the synchronous time sequence coordination capability and the channel bandwidth utilization rate in a multi-source heterogeneous environment are improved.

The invention belongs to the technical field of computers, particularly relates to a prefetch instruction management method, system and equipment, and aims to solve the problem of low instruction fetch efficiency of an instruction prefetch technology. The method comprises the following steps: receiving branch prediction information from a branch prediction unit, and performing label comparison on table entries in an instruction fetching target queue and the branch prediction information; under the condition that the table item is hit, querying an instruction fetching address corresponding to the branch prediction information in the hit table item; under the condition that the table item is not hit, a new storage table item is allocated to the branch prediction information, and an instruction fetching address carried by the branch prediction information is determined through the storage table item; in response to a received prefetching request sent by the prefetching unit, determining a target table item based on the prefetching request, and returning an instruction fetching address queried in the target table item to the prefetching unit; and storing the stable cache line in the target table item to a flow buffer area. According to the method, multiple prediction requests can be processed in parallel, the front-end throughput is improved, and the instruction fetching efficiency is improved.

The invention relates to the technical field of data processing, and particularly discloses an improved inter-ERP (Enterprise Resource Planning) system data migration and synchronization method, which comprises the following steps of S1, metadata acquisition and preprocessing; s2, field intelligent matching; S3, machine learning recommendation; s4, mapping confirmation and feedback; s5, converting rule configuration; s6, carrying out total migration; s7, performing increment synchronization; and S8, monitoring and alarming: counting delay, throughput and failure rate of increment synchronization in real time, and automatically sending an alarm notification when an abnormal threshold is triggered. According to the method, a complete technical system which is infinitely infinitely and covers the whole migration and synchronous life cycle is constructed from metadata acquisition to intelligent mapping, rule driving and full / incremental ETL (Extract Transform Load), and then to monitoring alarm and rollback management; the method is suitable for efficient, reliable and extensible data migration and synchronization between heterogeneous ERP systems, and provides solid support for enterprise informatization upgrading, system splitting / merging and big data analysis enabling.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and particularly relates to a neural network large model efficient reasoning method based on multiple GPGPUs. The method aims to solve the problems of high communication overhead, non-uniform load, low resource utilization rate, high data transmission delay and the like among multiple processors. Dividing a calculation task into a plurality of sub-graphs through static analysis and mixed granularity partitioning of a model calculation graph; distributing the sub-graphs to the optimal GPGPU based on a weighted cost function in combination with heterogeneous resource perception and a dynamic mapping strategy; a global pipeline scheduling plan is constructed by using communication topology perception, and calculation and communication overlap are maximized; data are loaded in advance through a host side hierarchical caching and asynchronous prefetching mechanism, and transmission delay is hidden; multi-stream concurrent execution and event-based lightweight synchronization are adopted on each GPGPU, so that waiting overhead is reduced. According to the method, the reasoning delay can be remarkably reduced, the throughput and the hardware utilization rate are improved, and the method has good adaptivity and expandability.

The invention belongs to the technical field of computers, particularly relates to a digital twin power plant infrastructure multi-source heterogeneous data real-time fusion method, and aims to solve the problems of high data fusion delay, semantic segmentation and poor system adaptability in the prior art. The method comprises the following steps: constructing a unified space-time reference frame to realize nanosecond-level time synchronization and space coordinate normalization; the method comprises the following steps: accessing and preprocessing multi-source data such as a building information model, an Internet of Things sensor, a construction log and a video stream, and generating a standardization unit with space-time metadata; performing semantic analysis and cross-modal feature alignment based on the power plant infrastructure ontology knowledge base; and millisecond-level dynamic fusion is realized by adopting an event-triggered streaming engine. According to the scheme, real-time fusion within 100 milliseconds is realized, the semantic alignment precision is 98% or above, the state confidence is 90% or above, the system throughput is improved by three times by relying on a cloud edge collaborative architecture, and precise twin mapping and intelligent decision making of the whole process of power plant infrastructure construction are comprehensively supported.

The invention relates to the technical field of underwater communication and positioning, in particular to a multi-mode underwater wireless communication system based on dynamic link calibration, which integrates underwater acoustic positioning guide, attitude adaptive tracking control, optical link enhancement and an error code correction mechanism. Comprising a central control core, an acoustic communication unit, an optical communication transmitting unit, an optical signal receiving unit, an optical control unit, a multi-axis attitude adjusting unit, a data fusion and verification processing unit and a high-speed modulation-demodulation and error code correction unit. And by using a double-shaft servo motor and automatic gain control, real-time target tracking and accurate alignment of optical signals are realized, FPGA and OFDM modulation are combined to optimize data throughput, and a data cross validation mechanism is introduced to correct error codes, so that the data integrity is improved.

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 key value cache grouping quantification method and device, a storage medium and electronic equipment. When the key value cache grouping quantification method provided by the specification is adopted to quantify key value cache data, key vector and value vector data are divided based on channel dimensions to obtain a plurality of grouped data; determining quantization parameters of the grouped data, and performing asymmetric quantization on elements in the grouped data based on the quantization parameters; finally, quantization results and corresponding quantization parameter partitions may be stored in physical blocks. According to the method, on the premise of ensuring the model generation precision, the video memory occupation of key value cache is greatly compressed, and meanwhile, the reasoning throughput is improved. Through fusion of dynamic channel grouping quantization and implicit inverse quantization, a key value cache quantization solution capable of guaranteeing the generation precision is provided for edge device end-side deployment, and the technical defect that the occupation scale of a video memory is too large when the occupation amount of the key value cache video memory is linearly increased along with the sequence length in the autoregression decoding process of a large language model is relieved.

The invention relates to the technical field of load balancing optimization, in particular to an intelligent computing center load balancing and energy-saving optimization method and system, and the method comprises the following steps: obtaining task execution duration and resource state data, calculating a multi-dimensional index, carrying out the weighted analysis, dynamically outputting a regulation and control scheme, and achieving the load balancing and energy-saving optimization of an intelligent computing center. According to the method, response time comparison based on a service level protocol is introduced, a task execution duration distribution structure is clearly recognized, the abnormal task recognition precision is improved, and the judgment accuracy of an overload trend is enhanced by comparing resource utilization rate changes of adjacent periods in the aspect of node state monitoring; in the task allocation process, the available memory of the node and the resource adaptation degree are combined, real performance load performance is obtained through energy efficiency and throughput data cross analysis, task allocation better conforms to the actual energy consumption level, the resource waste risk is effectively reduced, the response flexibility of a scheduling strategy to system load changes is enhanced, and the task allocation efficiency is improved. And the task execution efficiency and the energy efficiency distribution accuracy are improved.

The invention discloses a method for estimating the safety communication rate of a near-earth satellite by using an SDFS cooperating with a GLRT. The method comprises the following steps: constructing a communication system model between the near-earth satellite and a ground terminal; analyzing the signal and channel characteristics, and obtaining the distance between the nodes, the channel gain and the received signal distribution; under the condition that the transmitting power of each monitoring party is unknown, estimating the transmitting power by adopting generalized likelihood ratio test, and independently executing the generalized likelihood ratio test based on the observation data of each monitoring party; the superior manager performs joint judgment based on a soft decision fusion scheme in combination with the generalized likelihood ratio test result of each monitoring party and a joint threshold so as to estimate the detection error probability of the monitoring party; a joint optimization strategy of the transmitting power and the number of continuous transmission time slots is constructed, and the total throughput of the system is maximized under the condition that the cooperative concealment constraint is met; according to the invention, an innovative solution is provided for reliable communication of the near-earth satellite in a complex monitoring environment.

The invention discloses an automatic integrated system from an asynchronous pipeline to an asynchronous circuit, which adopts a modular architecture, takes a high-level structure description file in a JSON / XML format as input, and gradually maps to generate a gate-level netlist and a standard delay format file so as to be in butt joint with a commercial back-end design process. According to the method, asynchronous design based on an asynchronous structure can be automatically mapped into an asynchronous circuit, the automatic mapping process can seamlessly process multi-level design representation, the mapping result is stable and cooperatively operated, asynchronous circuit time sequence detection and delay matching are automatically completed, the design efficiency is greatly improved, and the design cost is reduced. An automatic delay matching mechanism enables the circuit to have a good data ready detection and handshake mechanism, and the characteristics of low power consumption, high throughput and no global clock drift sensitivity are realized; in addition, automation of asynchronous design key links such as structure abstraction, time delay modeling and testability insertion is achieved while design readability, hierarchical consistency and time sequence controllability are guaranteed.

The invention discloses a virtual power plant operation management and control method based on a block chain, original power data are compressed into Hash fingerprints to be chained through a collaborative architecture of an edge computing layer and an AI pre-decision module, the block chain network load is effectively reduced, and a dynamic elastic PBFT consensus mechanism adjusts the scale of verification nodes in real time based on transaction types, so that the efficiency is improved. And compared with a traditional fixed node consensus mechanism, the network communication overhead is reduced. And a hash value asynchronous pre-verification technology is adopted, so that the interaction rate of the multi-dimensional power data is improved, and the efficiency is improved compared with that of a conventional data uplink mode. Through the decoupling design of the intelligent contract execution flow and the consensus verification flow and in combination with the pre-decision data cache queue, the resource scheduling instruction issuing time delay is reduced. The block chain transaction throughput is improved and is effectively improved compared with a standard alliance chain architecture, and the technical problems that the consensus mechanism processing transaction speed of an existing block chain is limited and the high-frequency real-time data interaction requirement of a virtual power plant is difficult to meet are successfully solved.

The invention relates to the technical field of reasoning optimization, in particular to a PD separation reasoning framework optimization method oriented to a large language model, which comprises the following steps: S1, reconstructing a memory structure of a KV Cache, adjusting an original discrete storage structure allocated according to a model layer into a continuous storage structure allocated according to blocks, and changing the memory structure from [layer, k / v, block id, numhead, head, block size] into [block id, layer, k / v, numhead, head size, block size]; s2, dividing a short sequence, a medium sequence and a long sequence according to the length of the cue word, combining the short sequence into a Batch group, and preferentially extruding the short sequence and then processing the long sequence; and S3, deploying a hybrid throughput node cluster, and according to the input prompt word length dynamic allocation request, allocating a short request to a low throughput TP node of the throughput, and allocating a long request to a high throughput TP node of the throughput. The method is more suitable for a PD separated system architecture, and the transmission efficiency of the KV cache and the calculation efficiency of the GPU are improved, so that the throughput of the system is improved, and the maximum resource utilization is achieved.

The invention relates to the technical field of natural language processing, and discloses a workflow calling method based on a large language model. The method comprises the steps of receiving an initial task description text of a user, and decomposing the initial task description text into a discrete intention unit set through a semantic analysis engine; inputting a pre-trained large language model to carry out context association analysis, and generating a task node topological graph containing a hierarchical relationship; detecting a data transmission dependency relationship between nodes, and marking a strong association cluster with bidirectional data streams; according to cluster dynamic load parameters, automatically dividing parallel execution domains and distributing independent data transmission channels; and monitoring channel throughput fluctuation in real time, and triggering a channel switching protocol when the channel throughput is blocked. According to the method, the complex task execution logic and the data flow path are straightened out, the problems of dependency conflicts and resource allocation are solved, the orderliness and stability of workflow execution are guaranteed, and the automatic task scheduling requirements under various complex scenes are met.

The invention relates to the technical field of distributed computing, and discloses a heavy truck charging load scheduling-oriented distributed energy intelligent matching method, which comprises the following steps of: establishing a charging service computing power mapping table at a scheduling node; maintaining a shadow counter in a local memory, extracting a pre-estimated computing power consumption value according to an event type and accumulating the pre-estimated computing power consumption value to the shadow counter, and executing linear numerical deduction on the shadow counter according to a reference logic subtraction rate so as to simulate a scheduling data throughput evolution process; adjusting a linear deduction rate parameter according to the deviation between the state feedback data and the numerical value of the shadow counter; and distributing the charging matching task to a charging station edge computing node of which the shadow counter value does not exceed a preset logic saturation threshold value. According to the invention, through an open-loop estimation and closed-loop calibration mechanism of a local logic state, an instantaneous congestion risk caused by physical feedback lag is eliminated; and logic state consistency and self-adaptive distribution of the whole network computing power resources are realized.

The invention relates to the technical field of cloud storage, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a load and message queue regulation-based migration method, device, equipment and medium. Monitoring message backlog amount and consumption rate to generate message queue state information, generating migration regulation and control parameters based on the two types of state information, determining concurrency level and issuing rhythm by combining object size classification information, reducing issuing frequency and preferentially scheduling small objects when the message backlog amount is increased and oversize objects exist, and distributing the tasks to the working nodes according to the concurrency level and the issuing rhythm to execute migration, and adjusting migration regulation and control parameters based on execution state updating information. According to the method, concurrency and rhythm are regulated and controlled in real time, object size classification is combined, the problems that traffic is uncontrollable and throughput is affected by large files are solved, and efficient and stable data migration is achieved.

The invention relates to a dynamic speculation sampling method and system for large model reasoning. The method comprises the following steps: processing requests for multiple groups of input test data of similar scenes based on a predetermined application scene reasoned by the large model, obtaining the maximum throughput data volume of the throughput data volume of the target model in the conventional sampling mode and the maximum throughput data volume of the throughput data volume of the target model in the speculation sampling mode, wherein the maximum throughput data volume is larger than or equal to the maximum throughput data volume of the throughput data volume of the corresponding conventional sampling mode. Configuring a request number span sequence which is used for dynamic speculation sampling and is arranged from large to small and a draft token number sequence which is arranged from small to large and has corresponding length; aiming at a specific data processing request, determining a span value interval formed by adjacent request values of the current survival request number in the request number span sequence according to the current survival request number, and setting a draft token number for each request in the current survival request number based on a draft token value in a draft token number sequence corresponding to a lower request value of the span value interval.