2608 results about "Transmission delay" patented technology

The invention relates to the technical field of information retrieval, and discloses a distributed storage and indexing method, which comprises the following steps of: dynamically fragmenting an original file through an improved consistent Hash algorithm to generate a plurality of data blocks with timestamps; constructing a three-dimensional Bloom filter index matrix containing timestamps, data types and content features for the data blocks with the timestamps, associating a B + tree local index with an inverted global index through a hierarchical index structure, and establishing an index update priority queue by adopting a two-channel synchronization mechanism, performing real-time increment synchronization on the hotspot index through a heartbeat mechanism, performing batch synchronization on the cold data layer index according to a cold data synchronization period, predicting a data distribution probability through a distributed query statistics probability table during query, and initiating multi-path query in parallel based on probability weight, and the query path is dynamically optimized according to the node group storage medium type and the inter-node network transmission delay, so that rapid distribution adjustment and access of distributed storage are realized.

The invention relates to the technical field of communication, in particular to a low-delay data transmission system and method for marine observation data, and the system comprises a data collection module which collects the multi-modal data of a marine environment, and carries out the priority classification of the data according to the real-time performance and importance; the route optimization module dynamically adjusts a multi-path fragmentation strategy and single-path task distribution in combination with a reinforcement learning algorithm and path optimization data; the communication module supports multi-mode communication modes such as laser communication and low-orbit satellite communication, and monitors the link state in real time; and the data fusion and analysis module performs dimension reduction, fusion and real-time analysis on the data by using manifold learning and a graph neural network, and meanwhile, predicts a long-term trend through time sequence analysis to generate observation report data. The method effectively reduces the transmission delay of high-priority data, improves the response efficiency of the system to abnormal events, and is suitable for the scenes of marine disaster early warning, environment monitoring and the like.

The invention relates to the technical field of Internet of Things communication, and discloses an Internet of Things data information transmission method, a switch and a transmission system. The method comprises the following steps: acquiring multi-source data streams of Internet of Things terminal equipment, dynamically fragmenting according to types to generate fragmented data packets, matching a target transmission protocol for the fragmented data packets, generating a dynamic routing path in combination with a priority mark and the like, and transmitting the dynamic routing path. The system also has the functions of link quality evaluation and route adjustment, redundant coding packet loss recovery, transmission delay and integrity monitoring processing, encryption transmission based on security level, resource optimization, transmission strategy adjustment and the like. The switch is integrated with a data fragmentation module, a protocol matching module and the like. The transmission system comprises a terminal device cluster, an edge node network, a cloud server and a protocol conversion gateway. The data transmission efficiency, reliability and safety are improved, resource utilization is optimized, and the data transmission problem of the Internet of Things is effectively solved.

The invention discloses an electronic lead seal automatic detection method applied to logistics tracking, and relates to the technical field of Internet of Things safety monitoring. The problems of packaging data loss, tampering risk increase and real-time monitoring failure caused by wireless communication signal interference, transmission delay and state updating lag of an existing electronic lead seal are solved. The method comprises the following steps: fusing multi-physical field sensing data through a space-time correlation sampling algorithm, and constructing an anti-interference characteristic matrix; evaluating communication quality based on the dynamic probability network model, and triggering a multi-path fragmentation concurrent transmission strategy to avoid signal attenuation; a hybrid reasoning model is deployed at an edge end to screen key event data, and the transmission efficiency is optimized in combination with differential coding and an IEEE 1588 clock synchronization mechanism; the cloud end adopts a space-time diagram fusion analysis model to carry out cross-modal abnormal association scoring, corrects misjudgment and updates an edge model; according to the invention, the communication reliability, the real-time transmission efficiency and the anomaly detection precision of the lead sealing state data in a complex environment are obviously improved.

A system for the temporal ordering of multi-channel data streams, comprising the following: a multi-channel data reception module configured to receive real-time data streams from multiple sources, extract temporal metadata, and detect discrepancies in timestamps; a time synchronization module configured to align data packets using timestamp normalization, adaptive buffering, and AI-based predictive modeling; an AI-powered predictive modeling module trained to estimate missing or delayed timestamps and to reorder out-of-order data packets based on learned patterns; an adaptive buffer module configured to dynamically adjust buffer sizes based on real-time data arrival rates and network conditions; an ordering and output processing module configured to reconstruct the correct order of the data packets and transmit them to a processing or analysis system; and a module for error detection and correction that is configured to detect inconsistencies in timestamps, transmission delays and missing data points, thus ensuring increased reliability of the ordered data sequence.

The invention relates to a distributed large two-layer network intelligent routing method and system based on edge cloud nodes, and belongs to the technical field of computer networks. Aiming at the problems of slow routing decision response and low fault recovery efficiency of the existing network in a dynamic environment, the invention provides a method for periodically collecting link bandwidth utilization rate, transmission delay and other state information through an edge cloud node, uploading the state information to a central cloud platform to generate a global routing strategy and issuing the global routing strategy to the edge node, and triggering fault switching in combination with local dynamic threshold monitoring. According to the technical scheme, bidirectional communication between edge nodes and a central platform, a neural network model of spatial-temporal feature fusion and a path priority generation mechanism of multi-objective optimization are included. The method and the system are suitable for a large-scale distributed network environment, and network resource utilization rate and service continuity can be improved.

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 provides an intelligent short message link efficient detection method and system. According to the method, unmanned aerial vehicle cluster deployment is used for forming a dynamic communication topology, and the dynamic communication topology is used for monitoring the time delay mutation rate and the packet loss rate of a damaged short message link in real time and generating a state fluctuation sequence. When it is detected that the transmission time delay gradient exceeds an emergency communication threshold value, an abnormal marking mechanism is triggered, and abnormal association clusters and the space-time coupling relation of the abnormal association clusters are recognized by analyzing the data. Then, the information is bound with timestamps, and a block chain state change event is generated by using a multi-node parallel evidence storage technology; and adjusting the distribution density of the unmanned aerial vehicle according to a verification result, and generating a topology reconstruction factor so as to update a short message transmission path and optimize a communication network. According to the technical scheme provided by the invention, the stability and reliability of efficient detection of the short message link can be improved.

The invention provides an old cell environment sensor data processing method and system based on ultraviolet light communication. The method comprises the following steps: acquiring an environment feature set of an external facade of an old community building through a distributed environment sensor array, and constructing an ultraviolet channel reflection skeleton in combination with an obstacle coordinate; fusing the aging factor attenuation model and the physical space topological structure through a dynamic space-time grid division engine, and generating a multi-dimensional feature segment set carrying space-time attenuation weights; analyzing channel interference in real time based on the interference intensity probability matrix of the ultraviolet channel reflection skeleton, and generating a multi-band transmission configuration result; and calibrating multipath transmission time delay through a phase compensation factor to construct an ultraviolet light pulse sequence carrying a path feature code, and when the difference degree of adjacent units exceeds a critical condition, triggering a closed loop feedback mechanism and updating a link weight. According to the technical scheme provided by the embodiment of the invention, the efficiency and precision of old cell environment sensor data processing can be improved.

The invention is suitable for the field of GPU management, and provides an intelligent dynamic management method for GPU computing power and a cloud platform, and the method comprises the following steps: collecting task data and GPU state data, and generating a task queue and a resource allocation strategy in combination with a scheduling plug-in; based on the task queue and the GPU real-time load, dynamically adjusting a resource allocation proportion through reinforcement learning to analyze a task dependency relationship and generate a migration plan; optimizing a communication path and adjusting asynchronous transmission delay according to the task dependency relationship and the GPU communication topology, and outputting a synchronous state mark; and monitoring abnormity in combination with the synchronization state and the GPU hardware state, executing thermal migration according to the migration plan, performing video memory recovery, and updating the resource idle list. According to the invention, through algorithm innovation and hardware collaborative optimization, intelligent, dynamic and efficient resource scheduling in the multi-GPU system is realized.

The present invention discloses a method, device, equipment, medium and product for clock synchronization between clusters. The method is applied to a slave clock synchronization system in each cluster, and the slave clock synchronization system includes: a clock synchronization module and a calculation module; the clock synchronization module obtains the timestamp of the high-precision time synchronization protocol PTP message between the master clock server and the master clock server, and sends the timestamp to the calculation module; wherein the PTP message includes at least: a synchronization message, a follow-up message, a delay request message and a delay response message; the calculation module determines the corresponding master-slave clock deviation and transmission delay according to the received timestamp, and sends the clock synchronization signal determined according to the master-slave clock deviation, transmission delay and the local slave clock to the clock synchronization module; the clock synchronization module generates the corresponding slave clock second pulse according to the clock synchronization signal sent by the calculation module to complete the slave clock correction. This scheme can realize accurate clock synchronization between computing clusters in a multi-computing cluster system.

The invention relates to the field of 5G and digital information transmission, in particular to a 5G cloud-side collaborative content caching method, which comprises the following steps of: in a cloud-side collaborative scene, modeling user experience by comprehensively considering factors such as hit rate, transmission delay and traffic load; and the constructed cloud edge collaborative caching model is solved, and the solving process comprises content prediction based on Transform and solving based on a multi-agent reinforcement learning technology. The method comprises the following steps: splitting a cache model into a # imgabs0 # part and a # imgabs1 # part; solving # imgabs2 through a cache prediction scheme based on Transform, a deep neural network and super-dimensional calculation; and solving # imgabs3 # by adopting a deep reinforcement learning method. According to the method, the user preference information, the hot information and the user browsing history are comprehensively considered, and a differentiated feature extraction scheme is designed, so that relatively high prediction accuracy is achieved.

The invention relates to the technical field of ad-hoc network routing, in particular to an ad-hoc network routing method based on wireless link perception, which comprises the following steps of: calculating a link quality score of a node by monitoring and analyzing a wireless communication signal and spectrum environment data of the node, recording signal quality, interference condition and bandwidth use state of the node, and calculating a link quality score of the node; and generating link quality score data of the nodes. According to the method, the wireless link state of the node is accurately monitored and analyzed, so that the signal strength can be reflected in real time, the routing decision can be dynamically adjusted, in the aspect of spectrum resource allocation, the spectrum use efficiency of the network is optimized, optimization is performed based on a dynamic spectrum adjustment scheme and real-time feedback, and through a task migration mechanism, the resource allocation efficiency of the network is improved. The task can be automatically migrated to the optimal node, through the link state, the transmission delay is reduced, the network reliability is improved, the calculation efficiency is improved through the intelligent migration of the task load, and the calculation bottleneck problem in the network is effectively solved.

The invention relates to the technical field of wireless communication, in particular to an intelligent wireless communication control method applied to ships and boats. The method comprises the following steps: acquiring radar original point cloud data, an AIS ship attribute data set, a Beidou navigation state data set and night vision target feature data through a multi-dimensional sensor, and constructing ship multi-source standardized preprocessing data; therefore, the functions of multi-source heterogeneous data fusion, dynamic channel adaptive modulation, distributed relay communication guarantee, real-time air route dynamic planning, intelligent instruction generation and the like are realized; the problems of high data isolation, large transmission delay, rigid navigation path, slow obstacle avoidance response, insufficient formation collaboration and the like in the existing ship communication control technology are solved, and the communication stability, the navigation safety and the overall combat effectiveness of the ship formation in a complex dynamic environment are remarkably improved.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention relates to the technical field of terminal management, and particularly discloses a remote diagnosis management method and system based on a vehicle-mounted terminal, and the method comprises the steps: obtaining multi-source heterogeneous data collected by the vehicle-mounted terminal in real time; performing dynamic priority classification on the multi-source heterogeneous data to obtain a multi-source data priority set; constructing an edge-cloud collaborative multi-modal model according to the multi-source data priority set, and obtaining multiple pieces of dynamic data information according to the edge-cloud collaborative multi-modal model; and acquiring data association information and semantic information of each piece of dynamic data distribution information, and constructing a vehicle data association topological graph according to the data association information and the semantic information. According to the invention, the analysis and utilization capability of the vehicle data is effectively improved. The constructed edge-cloud collaborative multi-modal model can efficiently process multi-source data, reduce transmission delay and cloud burden, and improve real-time performance and decision accuracy.

The invention relates to the technical field of power distribution station supervision, in particular to an intelligent auxiliary and artificial intelligence visual gateway control method and system for a station building, which monitors network state parameters in real time, calculates a network quality score and dynamically selects an optimal transmission path based on a reinforcement learning algorithm. Comprising a high bandwidth mode, a low delay mode and a disaster recovery backup mode, stage processing and dynamic compression are performed on transmission data according to data types and network quality scores, link switching and a local caching mechanism are automatically triggered when a network is abnormal, and finally, multi-gateway load balancing and visual monitoring are realized through a software defined network architecture. The problem of transmission delay or interruption caused by the fact that a static data transmission framework cannot adapt to network state changes in the prior art is effectively solved, and the reliability and the real-time performance of data transmission are remarkably improved through dynamic path optimization, intelligent hierarchical transmission and a fault self-healing mechanism.

The invention discloses a ship management method and system based on digital twinborn scene construction. The method comprises the following steps: acquiring multi-source scene data of a target water area in real time, and constructing a corresponding ship management digital twin model; performing integrity evaluation on the constructed digital twinborn model, and performing defect compensation according to an evaluation result to obtain an optimized digital twinborn model; further collecting ship density and weather condition data of each sub-region of the target water area, constructing a network transmission delay prediction model, analyzing related data based on the model, and generating a network delay coefficient distribution diagram of the target water area; and according to the delay distribution diagram, determining the real-time updating lag amount of each subunit of the optimization model, and correcting the ship position information in the digital twin model. According to the method, the real-time performance and accuracy of the digital twin model can be remarkably improved, and the ship operation management efficiency and safety in a complex water area environment are enhanced.

The present invention relates to a hardware acceleration method, device, equipment and medium. The method comprises: obtaining a read-write command issued by a host; parsing the read-write command to obtain a parsing command; judging whether the parsing command is a read command; if it is a read command, judging whether the parsing command is a sequential read command; if it is a sequential read command, applying for a buffer from a read cache management engine, and constructing a pre-read command, carrying a buffer address and an LBA address; according to the pre-read command, caching data in a read cache buffer; submitting the parsing command to the read cache management engine for processing; judging whether the parsing command hits the cache data of the current read cache buffer; if it hits, directly returning the corresponding data from the read cache buffer to the host, and marking the command completion. The present invention improves the pertinence and efficiency of processing, shortens the response time of commands and the transmission delay of data, and improves the overall performance of the system.

The invention discloses a power line carrier communication protocol data link layer simulation method, which comprises the following steps of: constructing a physical layer interface simulation module, simulating power line carrier or wireless air interface transmission through TCP socket communication, and creating a high-priority task process to process time slot-level time sequence synchronization for triggering a physical layer transceiving action at a specified time slot; configuring a time sequence synchronization control mechanism, obtaining a timestamp carried by a beacon frame sent by a concentrator, calling a clock synchronization function to align a site local clock with the timestamp when receiving the head of the beacon frame, adding delay compensation before sending data, and delaying and starting a physical layer receiving action to offset IP network transmission delay when receiving the data; a data frame processing flow is realized, a header callback function is controlled through a registration frame to respond to a physical layer state change, TCP message data is cached by using an annular queue, and a physical layer interface simulation module analyzes a message, distributes a memory and processes a load at a specified time slot after time sequence synchronization.

The invention discloses a multi-communication group fusion holding method and system for an intelligent door lock, and the method comprises the steps: building an independent multicast tree taking a feature vector as an identifier according to a communication group request initiated by each main control door lock; generating a multicast message containing a target communication group feature vector and a digital signature according to a merging request sent by the first master control door lock; according to feature vectors exchanged by the master control door locks of the two parties through a secure channel, a target master control door lock initiates feature interception registration, and negotiates and establishes a boundary feature tag rewriting rule; and according to the established forwarding path and the rewriting rule, the audio and video streams of the communication groups of the two parties are subjected to feature tag rewriting through the network boundary router, and then bidirectional intercommunication is realized. By utilizing the embodiment of the invention, the dynamic security isolation and seamless fusion of multiple communication groups can be realized, the network load and transmission delay are reduced, meanwhile, the terminal door lock can realize audio and video intercommunication without sensing the network address of the opposite end or changing the configuration, and the expansibility, the security and the user experience of the system are improved.

The invention provides a reliability-aware dependent task unloading method based on topology reconstruction in industrial internet edge computing, which comprises the following steps of: constructing a system model covering an edge cloud network platform, an industrial cloud platform and internet of things equipment, and establishing a transmission delay model and a reliability model; constructing a task unloading mathematical model with the maximum reliability level; the method comprises the following steps: modeling a micro-service dependency relationship into a weighted directed acyclic graph based on a network flow theory, and carrying out topology reconstruction on micro-services applied to the Internet of Things through a Ford-Fulkerson approximation algorithm to obtain a micro-service grouping structure formed by minimum cut division; the micro-service grouping structure serves as priori knowledge to be input into the deep Q network, the deep Q network is used for solving a task unloading mathematical model, a task unloading strategy is dynamically adjusted, resource allocation is optimized, and an optimal calculation unloading scheme in the industrial internet edge calculation environment is obtained. The micro-service deployment is optimized, the communication overhead is reduced, and the system reliability and the resource utilization rate are improved through real-time network state dynamic decision making.

The invention discloses a data disaster recovery method supporting multi-backup path switching, which comprises the following steps: generating a dynamic granularity data fragment based on the sensitivity of input data and the characteristics of a storage medium, and generating a metadata label containing a data fragment identifier, storage preference and recovery priority; constructing a heterogeneous transmission path performance model through metadata labels, generating a path quality map, distributing an optimal transmission path for each data fragment, and monitoring path performance; detecting anomalies through the standardized deviation of accumulated transmission delay and migrating fragments; carrying out parallel verification by adopting incremental integrity verification and Merkle proof stored in the block chain, and preferentially retransmitting fragments which fail in verification; according to the invention, through the cooperation of dynamic data fragmentation, backup path optimization and recovery verification, the reliability and stability of data disaster recovery are ensured so as to meet the actual business requirements.

The invention relates to the technical field of data acquisition, in particular to a bus real-time data acquisition method and system, and the method comprises the following steps: collecting data from a plurality of sensors of a bus in real time; dividing the data into a plurality of categories according to the timeliness and importance of the data; multi-level dynamic compression algorithm selection based on data categories: adopting multi-level dynamic compression algorithms for different categories of data; during data transmission, a self-adaptive priority scheduling mechanism is adopted, and the uploading priority of different types of data is dynamically adjusted according to the current network bandwidth and the transmission delay; in a bus network, short-distance wireless communication is established among a plurality of buses, and data cached in one bus is shared to other surrounding vehicles through a bus-bus linkage mechanism; according to the invention, real-time sharing and temporary supplement of data are realized through short-distance wireless communication, the problem of data missing caused by network disconnection of a single vehicle in a traditional data acquisition system is solved, and the reliability of overall data is greatly enhanced.

The invention discloses an aircraft target tracking method and system based on compensation prediction, which are used for improving the target tracking precision in an image transmission delay scene. The method comprises the following steps: firstly, acquiring an image frame sequence of a target aircraft by using an airborne monocular camera, extracting a target center coordinate through a small target detection algorithm, and constructing a position sequence; the method comprises the following steps: extracting current high-frequency I MU data aiming at the condition that an image frame has transmission delay, inputting the current high-frequency I MU data into an LSTM-DKF model constructed by fusing LSTM and a delay Kalman filter, and predicting and generating a process noise and observation noise covariance matrix; and initializing a delay Kalman filter by using the matrix, and recursively predicting the target position during the delay period. And when the delayed image frame is received, backtracking and updating the state of the filter, recurring to the current moment again, and outputting the compensated target position. And finally, pixel deviation is calculated according to the compensation position, an aircraft tracking control instruction is generated, and high-precision target tracking is realized.

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 relates to the technical field of indoor environment monitoring, and discloses an indoor temperature and humidity acquisition system based on a distributed architecture, which comprises a distributed sensor node cluster, a data aggregation gateway and a central processing unit. The distributed sensor node cluster is deployed in different indoor areas to collect data, the data aggregation gateway preprocesses the data, and the central processing unit divides the areas according to spatial and temporal distribution characteristics of the data, constructs various data collection strategies, calculates node energy consumption estimation, data redundancy and transmission delay, and screens an optimal strategy to control node sampling and communication. The system is further provided with a fault-tolerant module, an edge calculation unit and the like, and can detect faults, predict trends, adjust strategy weights and the like. The system is comprehensive and accurate in data acquisition, good in data processing and transmission performance, intelligent in node management and high in environmental adaptability, has expansibility and collaboration, and can be widely applied to temperature and humidity monitoring of various indoor places.

The invention discloses a remote medical interaction method based on virtual reality, and particularly relates to the field of remote medical interaction.The method comprises the steps that by synchronously collecting multi-modal tactile data and transmission state parameters of a patient end, a joint feature vector is established, and the transmission delay of each modal tactile signal is predicted in real time. The method comprises the following steps: extracting sensitivity indexes of an operation stage and a tactile signal through a doctor end real-time image, calculating a reward value in combination with a double-branch regression network, updating a modal transmission strategy, and generating a dynamic priority distribution matrix; based on the matrix, optimizing a bandwidth slicing strategy and performing signal compensation by combining a real-time network state and delay prediction; in addition, switching and pathological mutation of surgical instruments are detected, priority distribution is dynamically adjusted, and efficient transmission and operation feedback of tactile signals are ensured.

The invention relates to the technical field of information processing, in particular to an Internet of Things data processing method and system based on a cloud platform, and the method comprises the following steps: based on multi-modal data uploaded by Internet of Things equipment, analyzing equipment node data synchronization delay and timestamp matching degree, carrying out the statistics of node communication frequency and equipment relevance, screening a communication optimal node pair, and carrying out the communication optimal node pair; and carrying out identifier distribution, calculating a data packet transmission success rate, and forming a node identifier distribution result. According to the method, the multi-modal data uploaded by the Internet of Things equipment is analyzed, the node communication efficiency is optimized, accurate allocation of communication resources is realized, the transmission delay and failure rate are reduced, the data transmission efficiency is improved, the data access popularity is identified, the utilization rate of storage resources is improved, the service life of the equipment is prolonged, and a high-frequency data retrieval path is dynamically optimized; the retrieval speed and accuracy are improved, the response time is shortened, and the data processing efficiency and the resource utilization rate are comprehensively improved from collection to storage, transmission and retrieval.

The invention discloses a dynamic load adaptive charging pile liquid cooling control method, relates to the technical field of thermal management, and solves the problems of temperature prediction deviation, local hot spot leak detection and protective supercooling caused by a simplified thermal model based on fixed parameters and key temperature points under rapid load fluctuation. Parameterization time-varying heat supply network parallel multi-rate observation is constructed, a heat exchange coefficient and node heat capacity are identified and calibrated through recursion with physical constraints, transmission time delay is merged into an extended state in an explicit mode, short-window high-frequency sampling and local temperature field reconstruction are triggered based on residual errors, and the time-varying heat supply network parallel multi-rate observation is achieved. A control sequence is issued in parallel by adopting hierarchical rolling type control with a second-order moment uncertainty constraint; based on the scheme, the short-term temperature field prediction precision and the local hot spot identification capability are improved, and the cooling resource directional utilization efficiency and the charging system operation continuity are remarkably improved.