511 results about "Processing delay" patented technology

The invention discloses a time synchronization method suitable for a satellite communication system, and the method comprises the steps: analyzing a forward broadcast signal, and obtaining the reference time of a network clock; constructing a geometric prediction handle based on satellite orbit data, and predicting one-way propagation time delay between a terminal and a satellite by using the geometric prediction handle; estimating the processing delay of the terminal according to the symbol rate and the equipment working condition of the terminal and the delay calibration data; fusing the network clock reference time with the processing delay estimate to synthesize a terminal local time; and the sending time of the return link is calculated based on the local time and the propagation delay prediction. According to the method, the synchronization precision can be improved to a microsecond level, the convergence speed in a dynamic scene is accelerated, adaptive compression of the guard interval is supported, and the stability and spectrum efficiency of a satellite communication system are effectively improved.

The invention relates to the technical field of intelligent monitoring, in particular to a multi-thread low-power-consumption intelligent monitoring system based on an AI processor. The method has the advantages that aiming at the problems of unbalanced computing power and power consumption, high multi-task processing delay and strong hardware dependence in the prior art, the NPU module of the RK3588 processor is combined with the INT8 quantitative model, so that the power consumption is lower than 10W under the 6TOPS computing power; a dynamic multi-thread scheduling mechanism is designed, parallel processing of more than eight paths of video streams is supported through binding of a priority queue and an NPU core, and end-to-end delay is compressed to be within 200 ms; a zero-copy video stream architecture is constructed, data transfer is eliminated through memory mapping, and preprocessing time consumption is reduced by 90%; an energy efficiency control module is integrated, the NPU voltage frequency is dynamically adjusted according to the load, and the energy efficiency ratio reaches 0.83 TOPS / W; space-time alignment of multi-model reasoning results is realized by adopting a frame ID synchronization technology, and the mismatching rate is lower than 0.1%.

The invention relates to the technical field of audio control, and discloses a low-delay audio input switching method and system, a storage medium and equipment, and the method comprises the steps: carrying out the parallel pre-initialization of a plurality of pieces of audio input equipment, and building an independent parallel audio data cache for each piece of equipment; monitoring the state of each audio input device and the audio stream quality in real time, and judging whether switching is triggered or not based on a multi-factor decision model; after the switching decision is triggered, seamless audio data stream switching is executed, and format unification processing and cross fade-in and fade-out transition are included; a unified equipment operation interface is provided through the hardware abstraction layer, and system resources are optimized and managed; a delay sensing closed-loop control mechanism is constructed, processing delay of each link is monitored in real time, a caching strategy, a processing algorithm and resource allocation parameters are dynamically adjusted, self-adaptive balance of low delay and high tone quality is achieved, and through the method, quick and smooth switching of audio input equipment is achieved, delay is remarkably reduced, and real-time audio experience is improved.

The invention relates to the technical field of electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention discloses a priority scheduling method and system for an on-orbit data processing task. The method comprises the following steps: constructing a DAG model, decomposing a macroscopic task into subtasks, defining a subtask dependency relationship, and endowing a subtask node with a priority attribute; setting or dynamically adjusting the priorities of the sub-tasks according to task service types, processing delay requirements or external instructions; maintaining a schedulable queue containing subtasks which have completed precursor tasks and meet scheduling conditions; calculating task scheduling priority indexes based on the queue, sorting, and selecting optimal calculation resource allocation; the priority is dynamically adjusted according to task waiting time or an external instruction during execution, and hunger of low-priority tasks is avoided; the multiple processing units execute the high-priority tasks in parallel according to needs; and after the task is completed, updating a subsequent node dependency state, and adding a new schedulable task cycle. According to the method, high-priority task resource allocation can be guaranteed preferentially, low-priority task hunger is avoided, and the overall throughput rate of the system and the self-adaptive capacity to on-orbit diversified tasks are remarkably improved.

The invention relates to the technical field of data processing, and discloses a mine production-oriented multi-source data acquisition and edge calculation fusion device, which comprises a clock synchronization access module, a data governance control module, a flow pipeline backpressure module, an AI reasoning optimization module, a rule closed-loop control module, a message safety uplink module and a center training iteration module, according to the method, the problem of difficulty in data fusion of multi-source equipment is solved, and the consistency of heterogeneous data in time and semantic dimensions is ensured; the stability of an edge computing system under a complex working condition is improved, and data loss and processing delay are avoided; collaborative decision-making of the AI model and the rule engine is realized, and the risk of false report and missing report is reduced; the security and reliability of the data transmission process are ensured, and local autonomy when the network is abnormal is supported; a model automatic iteration mechanism is established, and the ability of the system to adapt to different working conditions is improved.

According to the heterogeneous multi-core chip power consumption state control system and the heterogeneous multi-core chip provided by the invention, the power consumption state request of each sub power supply domain is received through the set system hardware power consumption management module, and the driving signal is sent to the corresponding sub power supply domain according to the preset hardware time sequence, so that the corresponding sub power supply domain enters the target state; generating a PMU control signal; and the power management module drives an internal voltage regulator to execute corresponding regulation operation according to the PMU control signal. A special control processor (SCP) is not needed, the system hardware power consumption management module realized by pure hardware is adopted to directly control the power consumption state of each sub power supply domain in the heterogeneous multi-core SoC, the software processing delay is eliminated, the response speed is high, the static power consumption of the power consumption management module is remarkably reduced, and the system is particularly suitable for an ultra-low power consumption scene and has a wide application prospect. And hardware-level surge current protection is realized in a multi-core power-on process.

The invention relates to the technical field of model training, in particular to a hotline platform label model training method based on a deep neural network. The method comprises the following steps: preprocessing an input hotline work order text, and extracting channel, region, time and other structured metadata; performing semantic coding on the text by adopting a pre-trained BERT model, and dynamically fusing structured metadata and text features through a gating attention mechanism; performing field adaptive pre-training by using a hierarchical encoder, and outputting an enhanced semantic representation vector; constructing a label co-occurrence probability matrix and inputting the label co-occurrence probability matrix into the graph neural network for multi-label collaborative prediction; carrying out adaptive optimization by combining a focus loss function and a meta-learning framework; the teacher model is compressed into a student model through knowledge distillation, and the student model is deployed to an NPU hardware platform, so that real-time reasoning and online feedback closed loop are realized. According to the method, the work order classification accuracy can be remarkably improved, the order sending error rate and the processing delay are reduced, and the hotline service intelligent level and the operation and maintenance efficiency are improved.

Methods, systems, and devices for wireless communications are described. A remote unit (RU) may transmit, to a distributed unit (DU), an indication of a skew time parameter associated with providing compensated samples for a plurality of symbols associated with a slot. The RU may store a set of samples associated with a first subset of the plurality of symbols associated with the slot, the storing based on a processing delay associated with receiving demodulation reference signals during a portion of the first subset of the plurality of symbols associated with the slot. The RU may transmit, to the DU, a burst of compensated samples for the plurality of symbols in a transmission window that is offset in time from a beginning symbol of the slot according to the skew time parameter.

The invention discloses a low-voltage uninterruptible operation site visual monitoring optimization method based on an edge computing architecture, and the method comprises the following steps: constructing an operation scene graph analysis module, obtaining the distribution of operators, the operation type and the equipment voltage class information of each monitoring region, generating a risk class mapping graph, and carrying out the operation scene graph analysis module; positioning and initial identification of a high-risk area are realized; generating a risk perception matrix; constructing a priority queue of computing resources and risk levels; acquiring identification precision, processing delay and behavior capture integrity indexes, and establishing a task feedback mechanism; constructing a scheduling logic credibility evaluation module, analyzing a scheduling instruction, marking and recording an abnormal path, generating a scheduling behavior record library, and predicting and early warning a priority inversion risk; and iteratively optimizing scheduling model parameters, and dynamically updating risk scores and scheduling logic. According to the method, high-risk task resources are guaranteed preferentially, priority reversal is avoided, and the stability, robustness and safety response timeliness of the system in a complex operation environment are remarkably improved.

The invention belongs to the technical field of mobile edge computing, and discloses a task scheduling method based on dynamic cloud edge collaboration. According to the invention, through link-level modeling and graph neural network feature extraction, adaptive expansion of different numbers of terminals and servers is realized; according to the method, the problems of instability and over-estimation in training are effectively avoided by combining cutting updating of a near-end strategy optimization algorithm and a generalized advantage estimation method. According to the method, the elastic computing power of the cloud and the edge low-delay characteristic can be fully utilized, the dynamically changing network scale and terminal requirements can be adapted, flexible unloading of tasks and dynamic resource allocation can be realized, task processing delay can be effectively reduced in a dynamic environment, the resource utilization rate is improved, and the resource utilization rate is increased. And good expansibility and stability are maintained under the condition that the number of terminals and the scale of the server fluctuate, the problems of computing resource bottleneck and task scheduling stiffness existing in the virtual reality video service of the existing mobile edge computing are solved, and the interaction experience of the user is remarkably improved.

The invention provides an energy storage equipment management method and system based on the Internet of Things, and relates to the technical field of energy storage management, and the method comprises the steps: determining an energy scheduling target node and a node interaction strategy through a block chain network according to the real-time operation data of energy storage equipment collected by an Internet of Things platform, and generating a decentralized scheduling instruction; docking a target node edge calculation unit according to the instruction, and outputting network configuration parameters; global state monitoring is carried out through an end-edge-cloud collaborative architecture, and data of charging and discharging abnormity of a physical layer and communication abnormity of a service layer are acquired; generating an exception handling strategy based on an adaptive deep reinforcement learning model, recalculating an energy scheduling target node, forming a closed-loop control instruction, and feeding back the closed-loop control instruction to the block chain network; according to the invention, processing delay easily caused by dependence on a master-slave block chain structure in a shared energy storage scene is avoided; the adaptability to the dynamic environment is enhanced, the sudden electricity price change or equipment abnormity is handled through automatic adjustment, and the effect of decision making in the complex power grid environment is improved.

The embodiment of the invention relates to the technical field of data transmission security detection, and provides a data transmission detection method and device, electronic equipment and a medium, and the method comprises the steps: receiving target data which is transmitted by a transmitting end and carries a first check code, and obtaining the character length of the first check code; when the character length of the first check code does not meet the integral multiple of the parallelism degree of a cyclic redundancy check algorithm, a preset method is adopted to verify the first check code, and the preset method is a method for carrying out data frame tail zero padding and then backspacing on the first check code; and carrying out transmission anomaly detection on the target data based on a verification result of the first check code. Therefore, processing delay is greatly reduced, resource occupation and power consumption are reduced, and cost is reduced.

The invention provides an embedded system based on NVME oF RDMA. The system comprises an initiating end, a target end and an NVME-oF Target unit, wherein the initiating end is used for initiating a data reading request or a data writing request to a storage target of the target end; the target end is used for executing corresponding data operation according to the request of the initiating end; the initiating end is connected with the target end through an optical fiber; the NVMe-oF Target unit is arranged in the target end, and the NVMe-oF Target unit is arranged in the target end; according to the system, a software and hardware collaborative storage access architecture is constructed, and a kernel protocol stack bypass mechanism is adopted, so that the processing delay in a data transmission path is effectively reduced, the real-time requirement in an embedded scene is met, and the high-delay bottleneck of data transmission on a standard Linux platform is solved.

The invention provides an intelligent building fire safety early warning system based on intelligent fire protection, and relates to the field of fire safety, and the system comprises a distributed data collection module which is used for achieving data real-time collection and network disconnection local processing through heterogeneous sensor networking and dual-mode communication; the edge processing module is used for carrying out edge processing on the output standardized environment data flow by adopting a fire behavior triple verification model and a dynamic weight algorithm, and carrying out cross-node model sharing in combination with a federated learning framework so as to obtain a fire risk score and a fire source positioning coordinate; the decision execution module is used for dynamically planning an evacuation path; the feedback module is used for performing real-time rendering fluid dynamics fire spreading simulation on the evacuation path and generating multi-terminal early warning; and the dynamic evacuation optimization module simulates and optimizes the exit position and the path planning through fluid dynamics to obtain the optimized path planning. The method and the device are used for overcoming the defects of cloud processing delay and failure during network disconnection in the prior art.

The invention discloses an edge computing network task unloading and resource allocation method based on optimal service quality, which comprises the following steps of: constructing a system architecture comprising a cloud server, a plurality of edge servers and mobile equipment, and establishing a multi-dimensional system model covering task characteristics, service cache, communication transmission, computing resources, service cost and service quality; constructing an optimization problem aiming at maximizing the long-term service quality of all mobile equipment, and forming a mixed integer nonlinear programming model under the constraints of mobile equipment cost constraint, storage capacity limitation, bandwidth, computing resources and the like; a double-time-slot hierarchical decision-making mechanism is designed, and multi-dimensional joint optimization of service caching, task unloading and resource allocation is realized through a collaborative mechanism that short-term resource allocation is constrained through a long-term caching decision and short-term performance feedback optimizes long-term caching. According to the method, the overall quality of service (QoS) can be effectively improved and the task processing delay and energy consumption can be reduced under the condition that the cost of the mobile equipment and the system resource limitation are met.

The invention provides an expert network processing method based on a hybrid expert model, and relates to the technical field of artificial intelligence such as large models, deep learning, natural language processing and computer vision. The expert network processing method based on the hybrid expert model comprises the following steps: selecting a target expert network from a plurality of expert networks according to a first processing number of target sequence unit tokens respectively processed by the plurality of expert networks; obtaining a second processing number corresponding to the target expert network according to a subtraction result between the first processing number and the target processing number; selecting a target CPU corresponding to the target expert network from the plurality of candidate CPUs according to the second processing quantity; using the target GPU to process the first target token corresponding to the target processing quantity, and using the target CPU to process the second target token corresponding to the second processing quantity; and obtaining a target processing result according to the processing result output by the target GPU and the processing result output by the target CPU. The processing speed of the target token can be improved, and the processing time delay of the target token can be reduced.

The invention discloses a self-adaptive processing method of a Kafka message queue system, which is characterized in that the message throughput is predicted based on an AI prediction method, and a resource scheduling instruction is generated according to a prediction result and a performance target to control the consumption concurrency; after local anomaly detection model training is carried out on each distributed node, model updating data are uploaded to a server side for safe aggregation, and global model updating parameters are generated and returned to optimize the detection capability of each node; carrying out parallel consumption processing on the message according to the instruction, carrying out classification retry and isolation on errors, and collecting processing performance indexes; and finally, the collected indexes are fed back to the resource scheduling and model updating step to form closed-loop control. According to the method, distributed collaborative anomaly detection of dynamic resource allocation and privacy security is realized, the throughput of the system is remarkably improved, the processing delay is reduced, and the reliability, the adaptability and the operation and maintenance automation level of the system are enhanced.

The invention provides a conveying control method for an electric energy meter tray. The conveying control method comprises the following steps: determining a path node and a target station which the electric energy meter tray needs to pass through, and a scheduling adaptation relationship among the three; according to the scheduling adaptation relation, constructing a three-dimensional state tensor so as to perform dynamic modeling on the scheduling intention of the electric energy meter tray, and constructing a task priority scoring function; acquiring a currently conveyed electric energy meter tray and a physical position, current to-be-processed quantity of each target station, current cache quantity of each path node and current congestion condition to the target station, and combining the maximum processing quantity of each target station, the physical position of each path node and the corresponding flow velocity to obtain a task priority score function; and obtaining a task priority score so as to perform task optimization control on each electric energy meter tray which is being conveyed at the current moment. By implementing the method and the system, the conditions of path congestion change, load of the target station and the like can be dynamically adapted, so that the problems of resource waste, task processing delay, production efficiency reduction and the like are solved.

The invention discloses a multi-node signal source cooperative processing method based on a POST-INNA integrated module, and relates to the technical field of multi-node signal source cooperative processing, and the method comprises the following steps: obtaining an initial network topology of a plurality of node signal sources; a graph structure self-healing mechanism is triggered through node offline monitoring, graph structure updating is carried out on the initial network topology, and the graph structure self-healing mechanism comprises edge weight distribution, topology reconstruction, isolated connection and load balancing; fusing the obtained signal source feature matrix with the adjacent matrix of the updated network topology to obtain a fused feature matrix; and predicting the signal state of each node through a preset first LSTM model according to the fusion feature matrix, starting an adjacent node cooperative compensation mechanism according to the predicted node signal state, and cooperatively processing the multi-node signal source. The method is used for solving the problems of node failure processing delay, low cooperation efficiency and poor adaptive capability of a multi-node signal monitoring system in the prior art.

Disclosed are methods and apparatus to process delay for PDCCH repetition. A transmission of a physical downlink control channel (PDCCH) repetition may be monitored. A multiplexing pattern of the PDCCH repetition and a physical downlink shared channel (PDSCH) within an overlapping symbol may be determined. A processing delay for a decoding of the PDCCH repetition, a decoding of the scheduled PDSCH, and an acknowledgement (ACK) preparation based on the multiplexing pattern of the PDCCH repetition and the PDSCH within the overlapping symbol may be determined. The PDSCH may be monitored after receiving the PDCCH repetition.

The invention discloses an ambiguity fixing method, device and equipment, and the method comprises the steps: obtaining an ambiguity floating point solution vector and a covariance matrix with a corresponding ambiguity mode under a current epoch based on Kalman filtering, and the ambiguity floating point solution vector comprises a plurality of original ambiguity; according to the covariance matrix, determining an arrangement sequence of the plurality of original ambiguity; according to the ambiguity mode and the arrangement sequence, multiple target ambiguity are selected from the multiple original ambiguity for two-stage fixation, and if two-stage fixation succeeds, fixed values of the target ambiguity after fixation are output; if the two-stage fixation fails, outputting an unfixed floating point value of the original ambiguity, and recording that the current epoch fixation fails; and repeating the above steps for the next epoch, and when the number of continuous fixed failed epochs is equal to a preset number, initializing parameters of Kalman filtering, and then repeating the above steps for the next epoch. According to the method, the calculation complexity and the processing delay can be reduced, and the adaptive capacity in a multi-frequency environment is enhanced.

The invention discloses a data processing system, method and equipment, a medium and a program product in the technical field of computers. A gateway, a metadata server and a data server are provided in the same system; the metadata server and the data server can realize separated storage of data and metadata; the transmission delay is shortened by sending the metadata processing request and the data processing request in parallel through the gateway, and the processing delay is shortened by accelerating the processing of the metadata server and the data server, so that the data read-write delay can be reduced.

The invention discloses a method for integrating an industrial field information system and a cloud platform based on edge computing. The method comprises the following steps: locally acquiring original monitoring data of a multi-source sensor of industrial equipment at an edge node; according to the method, the original data of the multi-source sensor is locally acquired and preprocessed through the edge node, and only the standardized time sequence data set and the feature vector are transmitted to the cloud, so that the original data transmission quantity is greatly reduced, and the network bandwidth pressure and the communication cost are relieved; feature engineering is carried out on the edge side in real time, a predictive maintenance model is deployed to carry out local reasoning, and an abnormal probability or an RUL predicted value is output, so that data processing link delay is remarkably shortened, and the real-time performance of health state assessment is improved; a maintenance decision is quickly triggered based on an evaluation result, so that instant response and accurate maintenance of equipment abnormity are realized, and fault expansion caused by cloud processing delay is avoided; meanwhile, an edge and cloud collaborative optimization model is combined with global data continuous iteration, and prediction accuracy and adaptability are improved.

The micro-service processing system provided by the embodiment of the invention comprises an event identification module which is used for defining a business domain event type and registering an event attribute structure; the event bus module constructs a distributed communication network containing a dual-channel message queue, and the event bus module implements partition routing according to event types; the event processing module is composed of an event producer and a consumer which are executed asynchronously, the event producer packages service actions into a standardized event data packet, and the consumer processes subscription events through a thread pool; the event storage module adopts a hierarchical log database for persistence of an event sequence and supports reestablishment of a service state according to a timestamp; and the security control module is used for implementing service authentication and event content encryption on a transmission layer. Through event bus dual-channel design, sender box transaction binding and hierarchical storage optimization, millisecond-level event processing delay is realized while the reliability of distributed transactions is ensured, and second-level state recovery can be realized based on a complete event log when a service fault occurs.

The invention discloses a differential privacy-based multi-dimensional dynamic load balancing scheduling scheme (DP-DMAPSA), which is constructed on a DPDK framework and is oriented to high-concurrency scenes such as a security crowdsourcing platform and the like. According to the scheme, multi-dimensional indexes such as queue depth, CPU utilization rate and processing delay are introduced, differential privacy mechanisms such as Laplacian and Gaussian are combined, noise disturbance is performed on load data, and reasoning attacks are prevented. In scheduling, the system dynamically selects a processing core according to a weighted hash value and a privacy index to realize flow consistency and load balancing. Meanwhile, a self-adaptive sub-flow granularity adjustment mechanism is designed, dynamic adjustment can be carried out along with flow and load changes, and the scheduling precision and the resource utilization rate are improved. Experimental results show that the scheme improves the system throughput while guaranteeing the data privacy, and is suitable for scenes of security crowdsourcing, data centers, edge computing and the like.

The embodiment of the invention provides a communication method and device based on a Modbus TCP protocol. According to the method, effective processing of sticky packet data is realized through pointer management and data length verification of the annular queue. Firstly, the total length of unprocessed data in a queue is calculated according to a head pointer and a tail pointer of an annular queue; taking the data length as a judgment basis, and judging whether the unprocessed data contains a complete data frame or not; if a complete data frame exists in the unprocessed data, reading and processing the data frame from the head pointer position of the annular queue; if the complete data frame does not exist in the unprocessed data, the processing flow is not triggered, next data processing polling is waited, it is ensured that only the complete data frame is analyzed, and abnormal processing caused by packet sticking is avoided. According to the method, through accurate pointer calculation and data length verification, the problem of packet sticking caused by a Nagle algorithm of a slave or host processing delay in Modbus TCP communication is effectively solved.

The invention provides an intelligent gridding comprehensive anti-misoperation locking system for a power distribution network, and the system comprises an optimization module which achieves the hierarchical decoupling design of the system through a distributed communication middle platform, an intelligent logic analysis engine and a programmable execution control unit. The deployment module is used for modularly deploying functional components based on the micro-service architecture, reducing the system coupling degree by using an asynchronous message queue as a cross-component communication bus in combination with a domain event-driven mechanism, and completing local logic judgment at an edge computing node through an FPGA (Field Programmable Gate Array); the network reinforcement module is used for constructing a heterogeneous communication network and guaranteeing the communication quality through a dual-channel redundancy transmission technology; the intelligent decision-making module is used for forming a self-adaptive decision-making model based on reinforcement learning and a knowledge graph technology; and the digital twinning verification module is used for establishing a power grid virtual mapping system through a digital twinning technology. Cooperation among modules of the system can be balanced, instruction processing time delay is shortened, communication reliability is improved, and efficient implementation of an operation scheme is guaranteed.

The invention discloses a low-delay interrupt processing method and system of an intelligent control center, and belongs to the field of industrial control computer system architecture. The method comprises the following steps: analyzing an interrupt request to obtain an event type identifier; based on a predefined event priority mapping table, matching the event priority into a corresponding interrupt priority; selecting a path from a pre-allocated and physically isolated hardware response path set according to the interrupt priority; distributing the interrupt request and the context data to a pre-bound processing core through the selected path; and the processing core executes the preloaded simplified processing routine to generate a driving instruction. Through combination of event semantic mapping and physical isolation hardware paths, a deterministic low-delay response channel is provided for core emergency interrupt, system reliability is improved through a load balancing mechanism based on cache consistency, the problem of large delay jitter of high-concurrency interrupt processing of an industrial intelligent control center is effectively solved, and the system reliability is improved. And the real-time performance of the control system is obviously improved.

The invention provides a metadata processing acceleration method, electronic equipment and a storage medium, and relates to the technical field of distributed storage. According to the technical scheme, the acceleration card obtains the metadata request, the metadata is cached to the cache module of the hybrid storage architecture based on the dynamic cache management strategy, and the acceleration card directly queries the cache and controls the network card to directly construct the response packet to return when the cache is hit, so that the data processing efficiency is improved. According to the method and the device, the problems of high delay and CPU resource scrambling and the like caused by the fact that a metadata request depends on a CPU to analyze a network protocol and inquire software cache in the prior art can be solved, so that the technical effects of remarkably reducing metadata processing delay, improving metadata throughput, reducing CPU occupancy rate, optimizing hardware resource utilization rate and reducing cross-node synchronous bandwidth occupancy can be achieved.