64 results about "Communication bandwidth" patented technology

This application relates to a multimodal Transformer parallel inference method and system based on dynamic operator decomposition. For each multimodal request, the method dynamically decomposes the computational tasks of the multimodal Transformer model at the operator level based on the network architecture configuration information of the multimodal Transformer model, the memory and communication bandwidth status of each computing device node in the target computing cluster, and the load characteristics of the multimodal request. For each operator, multiple candidate device allocation strategies are determined. The total cost corresponding to the operator under each candidate device allocation strategy is determined. Based on the total cost, the target device allocation strategy corresponding to the operator is determined from the multiple candidate device allocation strategies. Each operator and the data it processes are scheduled to the computing device nodes indicated by the target device allocation strategy for parallel inference computation. This method can reduce the end-to-end inference response latency of the model.

The application discloses an outsourcing encrypted database system and method based on a TEE server supporting a secure aggregation operation and a secure query operation. The system comprises a client and a cloud server configured with a rich execution environment (REE) and a trusted execution environment (TEE). The client generates a key pair, and adopts a Paillier threshold encryption strategy to split the private key into two shards, which are respectively sent to the REE and the TEE, and the data encrypted by the public key is stored in the REE. When responding to a query or aggregation instruction, the REE and the TEE cooperatively execute a cryptography operation protocol by using the private key shards held by each other through a hardware isolation mechanism. In order to further improve the processing efficiency, the application also introduces amortization optimization technology, which uses the property of homomorphic encryption to package multiple independent comparison tasks into a ciphertext for one-time joint decryption, thereby significantly reducing the calculation overhead and communication bandwidth.

The application relates to the technical field of artificial intelligence large model inference optimization, in particular to an intelligent body automatic learning method based on a limited computing power environment, which comprises the following steps: collecting real-time occupation data of computing power, display memory, display memory bandwidth and communication bandwidth in a real-time manner during an execution process; measuring and calculating display memory read-write bottlenecks and communication bandwidth bottlenecks to generate bottleneck analysis results; deleting redundant and repetitive instructions and operations in the inference process; adjusting a model parallel strategy, performing load balancing scheduling and communication optimization operations; and performing quantization and sparsification processing on model data; the application adopts a fractal thinking framework, each level follows unified optimization logic, and the optimization logic is look clear -> avoid waste -> improve utilization -> save resources; for the core bottleneck analysis dimensions of computing power, display memory, display memory bandwidth and communication bandwidth, a quantization formula model is established, resource bottlenecks in each stage are identified, an optimization direction is accurately positioned, blind optimization is avoided, and new models and new hardware can be understood quickly and conveniently.

This invention provides an adaptive quantization dual-teacher distillation federated learning method for heterogeneous environments, belonging to the fields of adaptive quantization, model compression, knowledge distillation, and federated learning. Its technical solution includes the following steps: S1, system initialization phase; S2, user extraction and model broadcasting phase; S3, local model training phase; S4, adaptive quantization phase; S5, global model update phase; S6, termination judgment phase, when the training rounds reach their maximum value. T If the condition is met, the federated learning task is terminated; otherwise, proceed to S2. This invention is applicable to heterogeneous edge device scenarios with limited computing resources and communication bandwidth, such as smart IoT devices, enabling high-precision, low-communication personalized federated learning while ensuring privacy and security.

The invention relates to the technical field of communication, in particular to a frequency domain resource determination method and device, a terminal, network equipment and a storage medium, and the frequency domain resource determination method comprises the following steps: determining at least one working bandwidth in a frequency domain range corresponding to the bandwidth of a service cell according to a predefined rule or indication information of the network equipment. According to the invention, the working bandwidth is in the frequency domain range corresponding to the bandwidth of the service cell, so that the working bandwidth can be smaller than the bandwidth of the service cell, and the terminal does not need to determine the communication bandwidth based on the bandwidth of the service cell, but can determine the communication bandwidth according to the predefined rule or the indication information of the network equipment. And determining at least one working bandwidth in the frequency domain range corresponding to the bandwidth of the service cell, and further communicating with the network equipment on the working bandwidth.

The application discloses a multi-agent distributed situation interaction device and method based on a multi-modal large model, and relates to the technical field of computer model application. The device comprises the following steps: constructing a double-layer distributed interaction network; setting an environment monitoring time window according to a preset feedback adjustment period, collecting the number of node survival, the available communication bandwidth value and the task urgency level as the environment state parameters; inputting the environment state parameters into a pre-constructed interaction strategy adjustment model to output the adaptive local node interaction strategy and the adaptive agent node interaction strategy in the next feedback adjustment period; and inputting the adaptive local node interaction strategy and the adaptive agent node interaction strategy into the double-layer distributed interaction network in the next feedback adjustment period to control the local consensus node and the agent node to respectively perform distributed situation interaction and output a unified task situation cognition benchmark. The application realizes high reliability, low delay and consistency guarantee of the distributed situation interaction of the unmanned cluster in a complex battlefield or dynamic environment.

This invention relates to the field of multi-agent system control. To address the consistency problem faced by networked multi-agent systems, it provides an observer-based multi-agent sliding mode control method and system under communication constraints. The observer-based multi-agent sliding mode control method under communication constraints includes establishing a model of a networked linear multi-agent system subject to external disturbances and with incompletely measurable states, and the communication topology between each agent; constructing distributed sliding mode observers for each agent; constructing an event-triggered mechanism based on exponential decay rate and a logarithmic quantizer model; and combining the distributed sliding mode observer, the event-triggered mechanism, and the logarithmic quantizer model to construct an observer-based event-triggered quantized integral sliding mode control law to control each agent, thereby achieving average consistency of the multi-agent system. It can effectively achieve average consistency of networked multi-agent systems under conditions of unmeasurable states, external disturbances, and limited communication bandwidth.

The application relates to an OpenGL multi-class object uniform drawing method based on VBO internal coding, which comprises the following steps: constructing a mixed data structure containing vertex coordinates, color components, texture coordinates and object type marks, and storing the mixed data structure in the memory of a VBO in an interlaced arrangement; defining a uniform vertex shader and a fragment shader; judging whether texture mapping is needed according to the object type marks during a rendering process; and deleting the VBO object and the shader program after the rendering is completed, and releasing the display memory and the memory resources. The application links map elements with different rendering logics, realizes the uniform drawing of heterogeneous objects under a single pipeline, fully utilizes the parallel processing capacity of a GPU, avoids frequent switching of the shader, effectively saves the communication bandwidth between a CPU and the GPU, simplifies the development difficulty of multi-map element mixed rendering in a complex scene, and facilitates the horizontal expansion of a system in large-scale geographic information visualization.

The present application relates to the technical field of unmanned aerial vehicle control, in particular to a control algorithm for cooperative inspection of unmanned aerial vehicle cluster, the present application is designed by innovative non-central architecture and double-neighbor system, when the node communication loss or failure of unmanned aerial vehicle cluster is up to 30%, it can still maintain more than 92% of task completion rate, fault tolerance is significantly better than prior art; its core only relies on three simple behavior rules, the requirement of single machine computing power and communication bandwidth is very low, it can adapt to low-cost civilian unmanned aerial vehicle platform, so that the cost of large-scale deployment is reduced by about 40%. Combined with dynamic weight adjustment mechanism and virtual leader guidance, the system can not only autonomously avoid dynamic obstacles in complex urban areas, but also accurately follow the preset inspection route, realizing the unity of flexibility and accuracy; and supports multi-hop Ad-hoc communication, maintains stable connection in the area without ground network; fault automatic switching, single machine exit does not affect the overall task; combined with fixed airport, it can realize 7*24 hours normal unmanned inspection.

The application discloses a kind of based on visual language model driven semantic communication multi-agent reinforcement learning collaborative control method, comprising: S1.Semantic perception: through knowledge distillation constructs light semantic encoder, real-time extraction first semantic feature;S2.Semantic communication: broadcast first semantic feature, receiving end according to channel quality adaptive degradation processing, generate second semantic feature and communication mask;S3.Cross-modal fusion: through bidirectional cross attention mechanism first, second semantic feature and dynamics state are fused, and output joint feature;S4.Policy optimization: based on first semantic feature constructs internal reward, combined with external reward to form total reward, using centralized training, decentralized execution paradigm, through heterogeneous agent near-end policy optimization algorithm carries out end-to-end training.The application solves the collaborative perception, semantic communication and decision optimization problem in the scene of limited edge computing power and extremely low communication bandwidth, and is suitable for heterogeneous multi-agent collaborative task.

The application discloses a power transmission and distribution network end cloud collaborative risk prevention and control method and system based on dynamic routing, relates to the technical field of power system automation, and comprises the following steps: collecting high-frequency time sequence electrical data in real time and constructing normalized time sequence characteristic vectors; inputting the time sequence characteristic vectors into a semantic routing network to calculate routing scores, comparing the routing scores with a preset threshold, and performing dynamic shunting after the comparison; when a local processing path on the end side is activated, a control instruction or a protection action signal is output by an end side execution model reasoning; when an end cloud collaborative processing path is activated, visual semantic features are extracted and jointly transmitted to a station end together with the time sequence features, and a station end cognitive large model is used for multi-modal fusion reasoning, to generate a treatment suggestion and return the treatment suggestion. Through the dynamic routing mechanism, the application realizes asymmetric collaboration of end cloud computing power, guarantees millisecond-level response speed, improves the research and judgment accuracy of complex faults, and significantly reduces communication bandwidth occupation and end side power consumption.

The application discloses a kind of unmanned aerial vehicle network topology reconstruction methods, comprising: obtaining unmanned aerial vehicle network information, ground user information;Wherein unmanned aerial vehicle is as air base station and provides communication service for ground user;For the recovery unmanned aerial vehicle network connectivity after unmanned aerial vehicle failure and ensure ground user full coverage and fairness requirement, the optimization problem of optimization target is maximized ground user total fairness throughput;The optimization variable of the optimization problem is the two-dimensional position of each unmanned aerial vehicle, the associated indicator variable between ground user and unmanned aerial vehicle, the communication bandwidth allocated to each ground user;Objective function is composed of the sum of the logarithmic value of all ground user actual throughput;Three-layer iteration method is used to approximate the optimal solution of the optimization problem;Optimal optimization variable adjusts the actual flight position of unmanned aerial vehicle, establishes the access relationship between ground user and unmanned aerial vehicle, allocates communication bandwidth for each user, to complete the topology reconstruction of unmanned aerial vehicle network.

This invention provides a multi-UAV fusion positioning system and device for ground targets. The method includes: acquiring scene sequence images and target distance information through an airborne observation platform of the UAVs, and calculating the initial position of the target in the world coordinate system; using a Kalman filter algorithm combined with variational Bayesian to jointly infer time-varying noise parameters and target motion state, and outputting a state estimation vector for each UAV; and employing a sequential Kalman filter architecture to use the estimation results of each UAV as correction input for time-series updates, thereby achieving fusion positioning of multiple UAVs. This system requires low communication bandwidth, has a low computational load on the airborne processor, can adaptively compensate for time-varying noise, and effectively handles the dynamic changes of heterogeneous data nodes. It is a multi-UAV cooperative positioning system that combines environmental adaptability and high robustness.

A dynamic memory system having multiple memory regions respectively storing multiple types of data. A controller coupled to the dynamic memory system via a communication channel and operatively to: monitor usage of a communication bandwidth of the communication channel; determine to reduce memory bandwidth penalty caused by refreshing the dynamic memory system; and in response, reduce a refresh rate of at least one of the memory regions based on a type of data stored in the respective memory region.

The present application relates to a kind of train schedule plan loading method, device, equipment and storage medium, train schedule has multiple time periods, the method includes the following specific steps: step S1: receiving the compressed schedule data sent by central control system, compressed schedule data omits the corresponding uplink time period and alternative time period of each time period;Step S2: according to compressed schedule data, the uplink time period and alternative time period corresponding to each time period are calculated, and the original schedule data is obtained according to compressed schedule data, uplink time period and alternative time period;Step S3: according to original schedule data control train travel.Compared with prior art, the uplink time period and alternative time period are calculated by train independently according to compressed schedule data in the present application, the communication bandwidth pressure of train autonomous operation is reduced, so that the stability of system is guaranteed, while the receiving time period of train schedule plan is greatly shortened, and the control efficiency of train is improved.

The application discloses an asynchronous federated learning method and system based on saliency deep compressive sensing, and belongs to the fields of artificial intelligence and communication technologies. The asynchronous federated learning method based on saliency deep compressive sensing comprises the following steps: initializing a global model and obtaining a local model gradient; performing frequency domain adaptive sparsification processing on the local model gradient based on a discrete cosine transform and an adaptive dynamic scaling mechanism to obtain frequency domain coefficients; compressing the frequency domain coefficients based on a structure-adaptive grouping deep compressive sensing mechanism, and generating compressed observation values in combination with precision-aware quantization; performing inverse transformation reconstruction on the compressed observation values to obtain approximate gradients; calculating adaptive gating weights based on an estimated reconstruction noise level, and updating an error feedback buffer and the global model by using the gating weights. Through the frequency domain driven saliency deep compressive sensing framework in combination with a noise gating aggregation mechanism, the application realizes efficient and robust asynchronous federated learning under extremely low communication bandwidth.

The application discloses a large model federated learning method and system based on low-rank subspace dynamic clustering. Each client of the application utilizes low-rank adaptation technology to perform efficient fine-tuning locally, and extracts a low-dimensional subspace of the parameter update quantity as a geometric representation through fast singular value decomposition; the server calculates the geometric similarity between the clients based on the subspace principal angle metric, constructs a bottom-up hierarchical clustering tree, and dynamically determines the optimal clustering structure by using the first-order difference of the link cost sequence; parameter aggregation is performed within the cluster to eliminate directional conflicts, and the intra-class aggregated model is used as the starting point for the next round of training, while a global model is introduced as a proximal constraint. While maintaining low communication bandwidth and computing cost, the application effectively aligns the optimization trajectory, eliminates the destructive interference of the gradient, and realizes the dual improvement of the generalization ability and personalized performance of the multi-modal large model in the heterogeneous data scene.

This application discloses a dynamic adaptive adjustment method, system, device, and medium for camera USB communication, relating to the field of machine vision. The method includes: acquiring the real-time available communication bandwidth of the camera; calculating the parameter information corresponding to the maximum value of an objective function based on a preset parameter search algorithm, using this as the target parameter; and adjusting the camera's frame rate and resolution according to the target parameter. Based on the aforementioned processing, by using a parameter search algorithm to detect camera bandwidth, the optimal resolution and frame rate of the objective function are calculated, and an efficient dynamic parameter adjustment method is designed, which can maximize bandwidth utilization while ensuring image quality. Therefore, dynamic adaptive adjustment of the camera's resolution and frame rate is achieved, which can fully utilize communication bandwidth while avoiding frame loss, thereby improving the camera's detection efficiency.

This invention belongs to the field of communication automation technology and discloses a method for edge intelligent collaboration and dynamic security scheduling in communication automation. The invention covers the entire process of communication automation from edge collaboration and bandwidth scheduling to security protection by constructing three core modules: intelligent collaborative scheduling of edge nodes, dynamic allocation and optimization of communication bandwidth, and coordinated adaptation of security protection and communication performance. It adopts core technologies such as edge node load-aware collaborative scheduling, dynamic prediction and adaptive allocation of communication bandwidth, and security threat classification and performance-adaptive scheduling. This overcomes the industry bottlenecks of traditional communication automation, such as inefficient edge collaboration, rigid bandwidth allocation, and a disconnect between security and performance. It achieves intelligent collaboration of edge nodes, dynamic adaptation of communication bandwidth, and a coordinated balance between security protection and communication performance, significantly improving the edge processing efficiency, bandwidth utilization, and security protection capabilities of communication networks. It adapts to the needs of multiple scenarios such as edge computing communication, industrial control communication, and mobile edge communication, and particularly solves the technical problems of edge node load imbalance, mismatch between bandwidth allocation and service requirements, and the impact of security protection on communication performance.

This invention relates to the field of big data mining technology, and discloses a spatiotemporal trajectory clustering method based on federated learning, which saves communication bandwidth and improves the privacy of data processing. In this invention, the client first samples trajectory points from the preprocessed trajectory; then, the trajectory is segmented based on these points, and spline functions are used to fit each segment. Finally, the start and end position information, start and end time sequence information, and parameter information of the corresponding spline function for each trajectory segment are encoded and sent to the server according to their respective trajectories and segments. During server processing, the decoded spline functions with the same trajectory number are concatenated according to time sequence and position to obtain the reconstructed trajectory. Then, cluster analysis is performed on the reconstructed trajectories to identify normal and abnormal trajectories.

The application relates to a communication computing resource allocation method and system for an industrial internet, which comprises the following steps: collecting heterogeneous flow characteristics and global resource reserves at the beginning of each scheduling period, generating a heterogeneous flow characteristic matrix and a global resource vector; performing emergency analysis on the matrix, constructing a communication computing joint utility function containing an exponential delay penalty term, and generating a game model instance; constructing a heterogeneous flow game framework based on the instance, taking communication bandwidth and edge computing power as a strategy space, superimposing global resource constraints and transmission-computation delay coupling constraints, and generating a game matrix with coupling constraints; calling a distributed iterative best response algorithm to solve a Nash equilibrium strategy, gradually updating the communication bandwidth and computing power allocation of each flow, and outputting a Nash equilibrium resource allocation vector; decomposing the vector into communication and computing instructions for execution, collecting actual feedback signals, and updating the penalty factor coefficients in the game model.

The application relates to a multi-modal Transformer parallel inference method and system based on dynamic operator splitting. For each multi-modal request, based on network architecture configuration information of a multi-modal Transformer model, memory and communication bandwidth states of each computing device node in a target computing cluster, and load characteristics of the multi-modal request, the multi-modal Transformer model is dynamically split at the operator level for computing tasks. For each operator, a plurality of candidate device allocation strategies corresponding to the operator are determined. The total cost corresponding to the operator under each candidate device allocation strategy is determined. Based on the total cost, a target device allocation strategy corresponding to the operator is determined from the plurality of candidate device allocation strategies. Each operator and data processed by the operator are scheduled to each computing device node indicated by the target device allocation strategy, and parallel inference calculation is performed. The method can reduce the end-to-end inference response delay of the model.

The application provides a vehicle networking image encryption authentication method based on AFCSM and selective retransmission, and a vehicle networking image encryption authentication system based on AFCSM and selective retransmission covers a complete closed-loop process from data collection to final delivery; first, the original visual information collected by data sources such as roadside units, vehicle-mounted units and law enforcement departments is gathered to a sender, an initial key is generated through SHA-512 and 4 rounds of Feistel network, and the image is processed in combination with a 2D-AFCSM hyperchaotic sequence. In the encryption stage, the system uses an 8*8 block authentication mechanism to embed SHA-256 codes into the image bit plane, and cooperates with the odd-even bit plane separation technology and the random neighbor diffusion algorithm to generate a ciphertext image, and then the ciphertext image is transmitted through the V2X network; through the selective retransmission mechanism, the communication bandwidth cost in the vehicle networking environment is greatly reduced while ensuring that the image is successfully repaired and restored, and the balance problem among confidentiality, integrity and bandwidth efficiency in the vehicle networking visual transmission is effectively solved.

The application discloses a kind of unmanned aerial vehicle cluster multi-view target identification method based on adaptive segmentation inference, applied to mobile communication and artificial intelligence fusion technical field, for the computing resource limited, communication bandwidth limited and real-time requirement higher problem that unmanned aerial vehicle cluster faces when executing target identification task in complex dynamic environment, the application first constructs a kind of distributed inference system framework based on unmanned aerial vehicle cluster, including a cluster head node and multiple member nodes, uses multi-view convolutional neural network as target identification model;Then an optimization model with the minimum system inference delay as the target is constructed, the model segmentation position is determined for each member node respectively, and the application method realizes low latency, high efficiency target identification, and is suitable for emergency rescue, maritime search and rescue, battlefield reconnaissance, intelligent inspection and complex environment perception and other application scenarios.

The application discloses a multicast communication security guarantee method, comprising the following steps: a multicast sender periodically sends multicast messages; after a receiver receives the multicast messages, if there is no security check requirement for the received information, the receiver directly uses a security code to check the message integrity, directly uses the message after checking the message disorder, deletion and repetition according to a time stamp and a serial number; and if there is a security check requirement for the received information, the receiver performs security check, including verifying a security signature by using a pre-configured encryption algorithm and key, checking the message integrity, disorder, deletion and repetition, and performing delay check. The application can save communication bandwidth, reduce the number of links and simplify link management based on multicast mechanism transmission of security information.

The application belongs to the technical field of server control, and particularly relates to a computing and storage split type server control method for low-delay interaction, which constructs a split type architecture with separated computing and storage, monitors interaction requests in real time, extracts type features, data volume space-time distribution features and priority decay coefficients to construct a request feature vector, dynamically allocates communication bandwidth based on the features, corrects the allocation result in combination with the priority decay coefficients, predicts data access probability through an LSTM model fused with an attention mechanism, preloads target data shards to a multi-level cache according to access heat, collects computing node data in real time to perform dynamic health score, and selects an optimal target node to migrate interaction requests and associated data based on a real-time communication delay matrix when the score is lower than a threshold. The application significantly improves the low-delay interaction performance of the server, improves bandwidth utilization and data access efficiency, and guarantees stable operation of the system through dynamic bandwidth allocation, intelligent preloading and node health management.

The application discloses a lightweight federated graph autoencoder training method for edge computing, belongs to the field of distributed machine learning and graph neural network, and comprises the following steps: in view of the problems of sparse edge-side graph data, limited communication bandwidth and unstable model training, local graph data representation is enhanced by extracting and fusing high-order triangular motif structures on the client side; a residual sparse compression method with key layer protection and error accumulation mechanism is adopted to greatly reduce the communication data volume and ensure the effectiveness of gradient updating; and a multi-stage scheduling strategy containing warm-up training, periodic full synchronization and learning rate decay is combined to inhibit model drift and ensure stable convergence. The application can effectively reduce communication overhead and improve the training efficiency and node clustering accuracy of the model in the edge environment on the premise of protecting data privacy.

The present invention provides a communication management device that can set a bitrate according to the communication bandwidth even when the communication bandwidth between the mobile device and the ground equipment fluctuates, thereby reducing distortion of images transmitted from the mobile device. [Solution] A communication management device for determining the bitrate when transmitting images from a camera 11 mounted on a railway vehicle 10, comprising a video transmission central control unit 21 which includes a prediction unit that predicts the communication bandwidth based on information about the state of the railway vehicle 10, and a determination unit that determines the bitrate when transmitting images from the railway vehicle 10 as a bitrate setting value based on the predicted communication bandwidth.