653 results about "Processing delay" patented technology

The invention relates to a network real-time synchronization communication system, and relates to the technical field of computer network communication. The system comprises four core modules: a hardware optical IO synchronization module which integrates a multi-wavelength optical transceiver array and an anti-jitter circuit and provides a nanosecond global clock signal, and the synchronization precision is less than or equal to 15ns; the time sequence arrangement module is used for dividing a fixed time window and a dynamic buffer window based on a time sensitive network, supporting a priority preemption mechanism of the SRIO bus and realizing deterministic data transmission; the distributed RTDATA nodes replace a traditional single-board computer, a heterogeneous computing unit and an intelligent storage controller are integrated, and protocol stack processing delay is reduced through the zero copy technology; according to the dynamic multicast routing system, a safely isolated multicast tree is constructed as required, and link load distribution is optimized in combination with machine learning. Through collaborative design of hardware optical synchronization and dynamic data arrangement, the problem of real-time performance reduction caused by multi-node communication concurrence is solved.

An adaptive adjustment method of packet-level transmission priority for delay guarantee in programmable networks. The method realizes the adaptive adjustment of packet-level transmission priority in programmable networks from three aspects to satisfy the preset transmission delay requirements, namely: adaptive adjustment of priority in programmable switches based on processing delay state and switch queuing state, adaptive adjustment of priority among programmable switches based on transmission delay in upstream switches, and adaptive adjustment of global parameters based on packet transmission delay satisfaction, so as to realize the packet-level transmission delay guarantee in different positions with different time scales. In addition, for a scene with a large number of time-sensitive flows, based on the optimization model and heuristic algorithm, the optimization deployment of time-sensitive flows can be realized in polynomial time under the condition of limited network resources to improve the overall extent of delay satisfaction.

The invention discloses a road environment monitoring terminal based on the Internet of Things, and relates to the technical field of traffic environment monitoring. The road environment monitoring terminal based on the Internet of Things comprises an environment monitoring module, an edge computing module, an Internet of Things communication module, a cloud analysis module, an intelligent linkage module, a power supply module and an equipment self-maintenance module. According to the invention, real-time preprocessing and intelligent analysis are carried out on the environmental data through the edge calculation module, abnormal modes in the environmental data are rapidly identified, and a real-time analysis result is generated, so that the data transmission bandwidth demand and the processing delay are greatly reduced; the cloud analysis module combines historical data and real-time data, and provides a global evaluation result based on a deep learning model, thereby supporting the intelligence of traffic management decisions.

The invention relates to the technical field of processing systems, and discloses a multi-modal data feature fusion processing system and method, and the system comprises an intelligent acquisition subsystem, a self-adaptive extraction subsystem, a dynamic fusion training subsystem, an intelligent generation subsystem, and a cross-modal AI data center. The intelligent acquisition subsystem is integrated with a multi-modal data perception engine, realizes dynamic data acquisition and preprocessing, and comprises an AI intelligent acquisition module, a first AI big data module and an intelligent communication interaction module. According to the technical scheme, the multi-modal data processing efficiency and quality are comprehensively improved, and the lightweight preprocessing model group performs exclusive optimization for different modals, so that the quality of the data input to the extraction subsystem is improved by 40%, and the feature extraction accuracy is averagely improved by 22%; the intelligent communication interaction module supports cross-subsystem real-time instruction interaction and load balancing, the data transmission efficiency is improved by 60%, and the high-concurrency scene processing delay is reduced to be within 150ms.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate.According to an embodiment of the present disclosure, proposed is a method for reducing processing latency which may occur when CA or DC configured for a terminal is activated or deactivated.

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 discloses a service request scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: selecting at least one candidate service instance for processing a business request from a server cluster based on the business request to be scheduled; obtaining computing power resources and network resources of each candidate service instance; selecting a target service instance from the candidate service instances based on the computing power resources and the network resources; calculating an optimal path from the current service node to the target service instance based on the network resources; and forwarding the service request to the target service instance based on the optimal path. According to the technical scheme provided by the invention, the response time and the processing delay can be reduced, and the service quality and the user experience satisfaction can be improved.

The invention discloses a queue management method of a storage device, an electronic device and a storage medium, and relates to the technical field of data processing. When the storage device is configured, a host only needs to send an instruction containing a batch operation type and a queue parameter storage address; triggering the storage device to obtain queue parameter information of a plurality of input and output queues in batches from a specified address, and completing creation or deletion operation of all the input and output queues at one time; according to the method and the device, a plurality of commands which need to be issued one by one originally are simplified into single interaction, so that the communication times and data transmission redundancy between the host and the storage equipment are greatly reduced, the occupation of link bandwidth is reduced, the time of an initialization or reset process is remarkably shortened, and the data transmission efficiency is greatly improved especially in a scene with a relatively large number of queues. The problems of heavy interaction burden, low link efficiency, long processing delay and reset overtime risk in the prior art are effectively solved, and the technical effects of simplifying the interaction frequency and improving the configuration efficiency are achieved.

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 discloses an X86 industrial control mainboard identity authentication system based on multi-mode fusion, which relates to the technical field of mainboard identity authentication, and comprises the following steps: acquiring a secret key, generating a dynamic hardware fingerprint by combining cache topology and instruction delay, collecting physical unclonable function characteristics of power supply noise and clock jitter, and generating a circuit fingerprint; generating a firmware execution track, recording dynamic behaviors of a call chain and interrupt processing delay, and constructing a firmware runtime feature library; collecting spatio-temporal characteristics, generating environment fingerprints in combination with a geo-fencing technology, capturing time sequence characteristics, and generating a network behavior portrait in combination with a device topological relation; timestamp synchronization is carried out; simulating a fingerprint data tampering attack; and carrying out weighted fusion on the static features and the dynamic features. The hardware feature acquisition module, the firmware behavior acquisition module and the environment feature acquisition module are arranged, and physical unclonability of circuit fingerprints and time sequence features of firmware behaviors are combined, so that attacks are effectively resisted.

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 provides an unmanned aerial vehicle assisted private network task unloading and resource optimization method and device, and relates to the technical field of unmanned aerial vehicle communication. The method comprises the following steps: constructing a calculation task processing delay and energy consumption model based on an unloading strategy; an unmanned aerial vehicle trajectory optimization and user task unloading strategy optimization algorithm based on DDPG is constructed according to the model; establishing an unmanned aerial vehicle perception model, and generating perception calculation task data through the unmanned aerial vehicle perception model; a dichotomy cross entropy loss function is adopted to optimize decision making and unloading of an action space in a DDPG-based unmanned aerial vehicle trajectory optimization and user task unloading strategy optimization algorithm, and an optimal unloading strategy and an optimal unloading proportion are obtained; and repeatedly and iteratively executing the unmanned aerial vehicle trajectory optimization and user task unloading strategy optimization algorithm based on the DDPG until the award function converges, and generating an optimal unmanned aerial vehicle trajectory flight and user calculation unloading strategy. According to the invention, tasks can be efficiently distributed between the ground and the aerial platform.

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 discloses a self-adaptive micro-service deployment resource optimization method for cloud edge-end cooperation. The method comprises the following steps: 1) modeling a system model; 2) modeling a communication model; 3) modeling a user demand processing delay module; 4) deploying resource consumption according to a micro-service type required by user demand processing, and modeling a micro-service deployment resource consumption model and a user demand processing resource consumption model; 5) performing formulation definition on the micro-service deployment resource optimization objective function to complete heterogeneous constraint high-quality characterization; 6) aiming at the difference dimension of the multi-objective optimization function, finishing normalization processing of different dimensions; and 7) solving a multi-objective optimization problem by adopting a tabu search method to realize effective utilization of resources in a deployment process.The invention belongs to the technical field of system resource optimization of the Internet of Things, and solves the problem that the Internet of Things in the prior art has defects in the aspects of application space demand coupling, decentralized coordination and service difference.

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.

A user equipment (UE) is described. The UE includes a processor configured to determine a dropping timeline for low priority channel dropping when a low priority channel collides with a high priority physical uplink control channel (PUCCH) carrying a hybrid automatic repeat request-acknowledgement (HARQ-ACK). The processor is also configured to determine a processing delay for the high priority PUCCH when the low priority channel collides with the high PUCCH. The UE also includes transmitting circuitry configured to transmit the PUCCH carrying the HARQ-ACK based on the dropping timeline and the processing delay.

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.

Systems, devices, and techniques for wireless communications based on one or more transmission configuration indicator (TCI) states are described. A described technique performed by a user equipment (UE) includes receiving a first downlink channel based on a current TCI state; receiving a physical downlink shared channel (PDSCH) in a first slot, the PDSCH carrying a radio resource control (RRC) activation command in the first slot, the RRC activation command indicating a switch to a target TCI state; waiting until an end of a switching period triggered by the RRC activation command to use the target TCI state, the switching period being based on a RRC processing delay; and receiving, based on the end of the switching period, a second downlink channel based on the target TCI state in a second slot.

The invention provides a filtering method and device for channel estimation, electronic equipment and a storage medium. The method comprises the following steps: acquiring initial channel estimation of a reference signal, and determining optimal noise power in a current target channel scene based on the initial channel estimation; performing first-stage Wiener filtering on the initial channel estimation to obtain first filtering channel estimation; and performing second-stage Wiener filtering on the first filtering channel estimation based on the optimal noise power to obtain second filtering channel estimation. Therefore, according to the scheme, the optimal noise power in the target channel scene is acquired in advance, so that the second-stage Wiener filtering can be immediately carried out after the first-stage Wiener filtering is completed, the process of carrying out noise estimation on the reference signal after the first-stage filtering can be omitted, the processing waiting time is reduced, and the processing efficiency is improved. And the processing time delay is reduced, so that the two-stage filter circuit can be designed in a tight coupling manner to enhance the stability of the filter performance.