1792 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.

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 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 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 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 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 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 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.

The invention discloses a power grid fault positioning system and method based on traveling wave distance measurement, relates to the technical field of fault analysis, and solves the technical problems that traveling wave feature analysis, temperature compensation and high-precision synchronization technologies are not effectively integrated, and rapid and accurate fault positioning of a smart power grid is difficult to meet. Through the technical combination of dynamic temperature compensation, high-precision time synchronization and intelligent fault classification, the bottlenecks of traditional traveling wave distance measurement in the aspects of precision, reliability and intelligence are systematically solved, temperature sensors are deployed at key nodes of a cable, the temperature is monitored in real time, the wave speed is dynamically corrected through a formula, the overall error is reduced, and the fault location accuracy is improved. Meanwhile, an optical fiber two-way time transmission method is combined, transmission delay errors are reduced, synchronization reliability is improved, finally, a mapping library is constructed through laboratory simulation and field data, a Euclidean distance matching algorithm is combined, fault reasons are automatically recognized, and rapid positioning of the faults and accurate recognition of the fault reasons are achieved.

The invention discloses an offshore wind power fault monitoring method based on a knowledge graph and digital twinning, and aims to solve the problems that multi-source data acquisition is dispersed, the transmission delay is high, and real-time convergence and integration are difficult to realize in the operation state monitoring of offshore wind power equipment. According to the invention, the following modules are provided: a multi-source data acquisition layer is used for solving the problems of dispersed data acquisition and transmission lag and ensuring the real-time performance; the knowledge graph construction module is used for solving the problems of extraction, fusion and storage of equipment entity relationships; the digital twin modeling module realizes visual presentation and accurate simulation of the state of physical equipment; the fault monitoring reasoning module solves the problems of anomaly detection and root cause positioning; the intelligent early warning decision module determines an early warning level and efficiently matches a solution; the visual interaction module solves the problems of scattered information display and poor linkage, supports third-party docking, realizes accurate early warning and root cause positioning, improves the decision-making efficiency, adapts to severe environments, supports large-scale wind power plant monitoring, and reduces the operation and maintenance cost.

The invention discloses a power data security transmission strategy dynamic optimization method based on gray measurement, which comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data in a power system, and generating a time sequence data set and a structure attribute data set; s2, performing privacy protection and noise addition on the time sequence data set and the structure attribute data set by applying a differential privacy mechanism; s3, performing evaluation by adopting a gray scale measurement and calculation method, calculating a gray scale weight and generating a transmission strategy; s4, according to the gray scale weight classification data, adjusting a transmission path and a time sequence; s5, dynamically adjusting the differential privacy noise intensity and the gray scale weight according to the network bandwidth, the transmission delay and the data requirement; and S6, continuously monitoring the transmission efficiency and the privacy effect, and adjusting a transmission strategy and a protection mechanism in real time according to feedback. According to the method, the dynamic collaborative optimization of privacy protection and scheduling efficiency of the power data in the transmission process is realized by fusing a differential privacy mechanism and a gray calculation method.

The invention discloses an efficient photovoltaic inverter communication monitoring conversion method and system, and relates to the technical field of communication monitoring services. The efficient photovoltaic inverter communication monitoring conversion method comprises the following steps: S1, collecting and preprocessing link structure data and field feature data, and constructing a standardized communication transmission data set; s2, analyzing the transmission delay of each level of communication node, and dynamically adjusting a data packaging structure and a main / standby channel forwarding strategy; s3, evaluating the structure mismatching risk of the main channel and the redundant channel, and triggering a path switching and structure recombination process; and S4, integrating delay and mismatch risk analysis results, and dynamically optimizing a link rhythm distribution structure. The problems that delay and distortion are easily introduced due to the fact that inverter data transmission is packaged and decoded for multiple times in different levels of gateways, and an existing system lacks an end-to-end link performance collaborative awareness and optimization control mechanism from the edge side to a cloud platform are solved.

The invention relates to the technical field of multi-agent cooperative control, and discloses a multi-agent cooperative operation control method and system based on a wireless deterministic network, and the system comprises a time synchronization calibration module which is used for collecting the initial timestamps and network transmission delay data of all agent nodes, setting synchronization protocol attributes for calibration, and transmitting the initial timestamps and the network transmission delay data to a wireless deterministic network; establishing a collaborative operation network model; the task instruction distribution module associates the job task demand with the agent capability data, and generates a collaborative job instruction set through task decomposition; the path conflict detection module initializes parameters based on a network model and optimizes a path to obtain a collaborative optimization path; the collaborative state sensing module obtains real-time state data and analyzes and determines control strategy parameters; and the control parameter optimization module performs simulation verification and feeds back optimization parameters. According to the system, the precision, efficiency and reliability of multi-agent collaborative operation are improved, and the system is suitable for various complex operation scenes.

The invention relates to the technical field of audio and video coding, decoding and processing, provides an audio and video signal transmission method and system based on software and hardware cooperation of an autonomous controllable architecture, and solves the problem of low bandwidth utilization rate. The method comprises the following steps: synchronously acquiring bandwidth fluctuation, packet loss rate and transmission delay parameters of a mobile terminal link, and generating a network state level through association analysis; performing resolution scaling on a brightness component of a video frame, performing dynamic downsampling on a chrominance component, generating a resolution reduction frame adaptive to a network state, and generating a fault-tolerant coding stream by combining a packet loss rate regulation and control quantization step size, a frame group length and an inter-frame prediction mode; performing entropy decoding and motion compensation to align brightness and chrominance components, forming a decoding reconstruction frame, performing multi-scale feature extraction and detail compensation, generating an enhanced brightness graph, and performing independent reconstruction on chrominance decoding components; and outputting an original resolution video frame through color reconstruction fusion. According to the invention, high-definition video low-delay transmission under network fluctuation is realized, and both bandwidth adaptability and image quality restoration precision are considered.

The application discloses a 5G network slice access control method based on service type, which comprises the following steps: 1) in a 5G network supporting multiple network slices, the type of a newly arrived service is judged, and the service is divided into a time delay sensitive service and a non-time delay sensitive service; if the service is the time delay sensitive service, step 2) is executed; if the service is the non-time delay sensitive service, step 3) is executed; 2) a bearing slice of the time delay sensitive service is selected; 3) a bearing slice of the non-time delay sensitive service is selected; 4) the next service needing transmission is waited for, and step 1) is executed. The application can minimize the time delay of the time delay sensitive service, realize the relative fairness of the time delay of various services, minimize the transmission waiting time of the non-time delay sensitive service, and improve user perception.

The invention relates to the field of network communication, and discloses a multi-source heterogeneous network data cooperative transmission method based on dynamic multi-dimensional evaluation and intelligent disaster recovery, which comprises the following steps: collecting system network parameters of a 5G network and a long-distance wired network in real time through a software definition interface module; performing weighted evaluation on the acquired system network parameters based on a dynamic link selection engine, the evaluation dimensions including real-time bandwidth, transmission delay and current link traffic, and generating an optimal communication link combination scheme; carrying out fragmentation and protocol adaptation on the data by adopting a general packaging framework and an intelligent label technology; distributing multi-path parallel transmission according to a priority strategy; at a receiving end, data recombination and disaster recovery supplementary transmission are realized through network coding and multi-path cooperation; and the evaluation weight is dynamically optimized in combination with reinforcement learning. According to the invention, the problems of rigid link selection, low protocol conversion efficiency and insufficient disaster tolerance in the prior art are solved.

This application provides a method for sending training data of an AI model, which is applied to a scenario in which a reporting network element of the training data sends the training data to a training network element of the AI model. After obtaining the training data, the reporting network element classifies the training data into reference data and non-reference data, and indicates the training data by sending first-type information and second-type information to the training network element. The first-type information includes full information of the reference data, and the second-type information includes incremental information of the non-reference data relative to reference data corresponding to the non-reference data. Thus to help reduce overheads of sending the training data, thereby helping reduce air interface overheads in an AI model training or updating process, reduce a transmission delay, and improve model training performance.

The invention provides a wireless sensing platform with an edge computing function, and relates to the technical field of wireless sensors. In order to solve the problems that in the prior art, a wireless sensor is large in data redundancy, high in transmission delay and weak in emergency recognition and response capacity in a complex scene, a joint processing mechanism combining a causal event recognition assembly, a causal data fusion assembly, an event separation assembly and a data distribution assembly is adopted. By detecting a causal change event in an environment and dynamically extracting and fusing sensor data with a strong causal relationship, redundant data transmission caused by emergencies is reduced, transmission delay is reduced, the robustness of causal data fusion in a concurrent event scene is improved, and the reliability of data fusion is improved. According to the invention, efficient and accurate sensing and response to emergencies in a complex environment are realized.

The invention discloses a time-space alignment-based vehicle-road cloud integrated end-to-end automatic driving device and method, and aims to solve the problems of limitation of a single vehicle intelligent end-to-end algorithm and incompatibility of fusion after vehicle-road collaborative perception. The device comprises a roadside device and a vehicle end, the roadside device extracts roadside features and transmits the roadside features, and the vehicle end compensates transmission delay through a space-time alignment network module, projects the roadside features to a unified BEV space, and inputs the roadside features into a prediction planning network module after dynamic fusion. The method comprises the steps of road side processing, vehicle end processing, space-time alignment, feature fusion and end-to-end planning, and multi-task loss function optimization is adopted. According to the invention, deep fusion of vehicle and road information is realized, heterogeneous sensor scenes are adapted, the vehicle end load is reduced, the long-tail scene sensing and planning precision is improved, and the reliability and safety of automatic driving are enhanced.

The invention relates to the technical field of coal mine telecommunication, and discloses a coal mine underground integrated private network coverage system based on a 5G communication and AR double-transmitting selective-receiving technology, an efficient transmission and baseband processing network is constructed by taking a macro base station as a core on the ground and matching with PTN equipment and a BBU, a 5G micro base station and an RRU are deployed under the ground for a complex environment, and low-loss signal coverage is realized through optical fibers. The AR double-transmitting selective-receiving module ensures that underground data are not lost and have no error through redundant coding and double-link transmission; the edge computing module sinks data processing to the underground, reduces transmission delay and optimizes resource allocation; and the system monitoring and interference suppression unit monitors the operation state in real time, performs multi-stage alarm and resists multipath effect and electromagnetic interference, and finally realizes integration of communication above and under the well, reliability of data transmission, high efficiency of resource utilization and stability of system operation, thereby providing solid communication support for safety production, equipment management and control and personnel scheduling under the coal mine.

The invention belongs to the technical field of ship management, and provides a combined intelligent ship water diversion system integrating differential positioning, a laser radar and a camera. In the shore end RTK-inertial navigation depth fusion positioning module, extended Kalman filtering fusion is carried out on data acquired by shipborne equipment, so that continuity of accurate positioning is realized; the construction of a color high-precision map is realized through a laser radar-monocular vision color high-precision modeling module, details such as wharf fender materials and signboard characters can be better distinguished, and the problem of shoreline modeling defects is solved; visualization of obstacles is achieved through the multi-scene dynamic obstacle detection and fusion module, meanwhile, through cooperation of multi-source data, the precision and stability of ship positioning are greatly improved, higher precision and reliability are achieved, and the precision of a guide instruction is improved; through cooperation of the 5G private network and the visualization module, the data transmission efficiency is improved, the timeliness is improved, and transmission delay can be avoided.

The invention belongs to the field of star flash mouse communication, and discloses a star flash mouse low-power-consumption communication system and method based on Polar code and SLE collaborative optimization, the system adopts a layered architecture design, collaborative optimization of energy efficiency and performance is realized through deep coupling of a physical layer and a link layer, and the performance of the system is improved. The core of the system is composed of three functional units including a Polar code coding module, an SLE control engine and a cross-layer optimization interface, and a complete closed-loop control system is formed. According to the invention, through deep cooperation of the Polar code and the SLE mechanism, the overall power consumption is reduced by 42.7% compared with the traditional scheme; according to the Polar code dynamic construction method based on track prediction, an ultra-low bit error rate of 10 <-5 > magnitude can still be kept in a 2.4 GHz complex interference environment, and in combination with signal-to-noise ratio self-adaptive coding parameter adjustment, it is ensured that transmission delay within 0.5 ms is always kept from a low-speed office scene to a high-speed electronic sports scene; in the aspect of engineering implementation, the method has the capability of quickly adapting to hardware platforms of different manufacturers, and a complete solution with high performance and low cost is provided for large-scale commercial use of the satellite flash technology in the consumer electronics field.

The invention belongs to the technical field of information and communication, and relates to an active user detection and data decoding method of an asynchronous massive machine type communication system. Aiming at the problem that the detection and estimation performance is reduced due to unknown user time delay in asynchronous transmission, the method comprises the following steps: initial estimation and preprocessing: detecting initial active users and transmission time delay thereof through correlation peaks, and whitening received signals; the iterative joint detection and channel estimation module is used for iteratively updating channel information and data symbol information for multiple rounds based on a factor graph and a message passing algorithm; the data decoding module is used for decoding data based on a soft decision device; the detection and decoding performance is improved by transmitting external information among the modules; and the time delay parameter updating adopts a greedy search algorithm to obtain the optimal time delay. According to the method, the active user detection rate, the channel estimation precision and the data decoding accuracy under the condition that the receiving and transmitting ends are completely asynchronous are improved.

The invention relates to the technical field of network communication, and discloses an AI load-oriented network topology awareness and communication optimization method, an AI load-oriented network topology awareness and communication optimization device and a related system. The method comprises the following steps: acquiring gradient transmission delay data among computing nodes in a distributed AI training system, and constructing a gradient distribution table of gradient tensors and RDMA intelligent network card queue resources; performing aggregation operation on the gradient tensor in the gradient distribution table on a programmable data plane of the RDMA intelligent network card to obtain pre-aggregated gradient data; calculating a transmission efficiency index of the pre-polymerized gradient data and solving to obtain a bandwidth allocation coefficient; and adjusting an MTU frame parameter, a WQE queue depth and a credit window of the RDMA intelligent network card according to the bandwidth allocation coefficient, and generating a hybrid execution scheme. According to the method, intelligent distribution of the gradient tensor between hardware unloading and software processing is achieved, and the performance close to the optimal performance is obtained under the condition that resources are limited.

The invention relates to the technical field of heterogeneous network optimization, and discloses a heterogeneous network dynamic routing collaborative optimization method based on reinforcement learning, which comprises the following steps: S1, deploying an agent at each node of a heterogeneous network, and initializing a state sampling period and an action space; s2, sensing the state of the heterogeneous network in real time and obtaining sensing data; s3, encoding the sensing data into a state vector; s4, designing a layered dynamic reward function, including short-term reward and long-term reward; dynamically setting a weight ratio according to the scene type and outputting the weight ratio as a comprehensive reward; s5, carrying out multi-agent collaborative decision-making, which comprises the following steps: constructing a local topological graph based on neighbor state information broadcasted by nodes; each agent selects an action based on the state vector through the DQN; and updating the network weight of the DQN by using the comprehensive reward. According to the invention, transmission real-time performance and sustainability can be considered, transmission delay and transmission cost are reduced, and the method is suitable for film and television production and industrial Internet of Things scenes with variable topology and complex interference.

The invention relates to the technical field of power internet of things, in particular to a broadband / narrowband self-adaptive switching power internet of things communication system and a control method, which intelligently identify the priority and quality of service (QoS) requirements of different services by collecting network states, terminal states and service flow data in real time. And an optimal communication mode switching strategy is dynamically generated based on a pre-trained machine learning model, seamless switching of broadband and narrowband communication links is realized, and the decision accuracy is continuously improved through a feedback optimization mechanism. According to the method, the problems that a traditional electric power Internet of Things communication mode is fixed and single and cannot intelligently adapt to services and network states are effectively solved, the system communication reliability is remarkably improved, the transmission time delay and energy consumption are reduced, and high-adaptability and high-reliability intelligent communication guarantee in a complex electric power scene is achieved.

The invention relates to the field of automatic control, and discloses an intelligent control dispensing method and system based on automatic optical detection, and the method comprises the steps: obtaining multi-source real-time data of a dispensing production line, carrying out the feature recognition processing through a preset feature extraction model, and obtaining the dynamically updated production state information; then interference filtering processing is carried out to obtain an effective data set, when it is detected that quality fluctuation exceeds a preset range, an alarm is triggered, deviation identification information is generated, self-adaptive parameter optimization processing is carried out in combination with equipment characteristics and historical operation records, and a control instruction is generated; a control instruction is issued to an execution unit through a double-channel transmission mechanism, transmission delay is monitored in real time, and channels are automatically switched; when the output deviation exceeds the tolerance range, a compensation instruction is generated, parameters of the feature extraction model are updated based on the novel quality fluctuation features, and the dispensing control process is iteratively optimized through closed-loop control. According to the method, the dispensing precision and the production efficiency can be remarkably improved.

The invention provides an edge computing remote medical task offloading method based on an improved PPO algorithm, and the method comprises the steps: dynamically selecting an optimal offloading mode through a PPO-MED intelligent decision module according to the characteristics of a medical task and a system resource state, and enabling the offloading mode to comprise a local computing offloading mode (LCO), an edge server offloading mode (ESO) and an edge intelligent preprocessing offloading mode (EIPO); a PPO-MED algorithm models a medical task unloading problem into a Markov decision process, and an unloading strategy is optimized through deep reinforcement learning. The improved PPO-MED algorithm realizes intelligent decision, can dynamically select the optimal unloading strategy according to the real-time system state, task urgency and resource availability, significantly improves the adaptive capacity and service quality of the system in a complex environment, compresses the original medical data into a plurality of orders of magnitude, and improves the reliability of the system. On the premise that the diagnosis accuracy is guaranteed, the transmission delay is remarkably reduced, and the method is particularly suitable for remote diagnosis application of basic medical institutions under the limited bandwidth condition.