51 results about "Large networks" patented technology

The invention discloses a forest fire smoke monitoring method and related device based on false detection feedback and model self-evolution, and the method comprises the steps: obtaining a video stream of a monitoring region, carrying out the preprocessing, inputting a target detection model and a scene understanding model, and judging whether a candidate alarm event is generated or not through consistency analysis; when a candidate alarm event is generated, a work order is generated for manual judgment, a real alarm executes a disposal process, and a false alarm is stored in a database; screening high-value false detection samples based on an information entropy priority strategy, storing the samples into a dynamic memory bank, selecting the samples to form an incremental training set, performing incremental training on a pre-training teacher model, and introducing low-rank constraint stability parameters; migrating the discrimination ability of the teacher model to the student model through knowledge distillation, wherein alignment of a channel dimension and a multi-scale space dimension is carried out; and redeploying the updated student model to execute target detection. The invention aims to solve the problems that the current monitoring system depends on manpower, the coverage capability is limited, the response is slow and the cost is high, and the traditional method is high in false alarm rate, easy to leak and misjudge, difficult in large-scale network edge deployment, limited in light-weight model discrimination capability, lack of linkage between alarm processing and model updating and the like, and more accurate and efficient forest fire smoke monitoring is realized.

The application belongs to the field of FPGA-based hardware accelerator design, and proposes a point cloud target detection neural network accelerator and an acceleration method based on FPGA. The convolution accelerator comprises an instruction decoding module, a convolution module, an accumulation module, a truncation module and an activation function. The intermediate value cache only temporarily stores the channels of one feature map, consumes less BRAM, and is suitable for accelerating the point cloud target detection neural network with large feature map size, large number of channels and large network size. The application has flexible configurability and excellent reusability, so the application can accelerate different convolution layers of the point cloud target detection neural network, and is suitable for being integrated as an IP into a large design, reducing FPGA resource consumption and improving resource utilization. Moreover, the weight and the input feature map share one data input channel, and the convolution intermediate value is temporarily stored on-chip BRAM, reducing the feature map carrying frequency and saving the FPGA interface bandwidth.

The present application is a kind of lightweight target detection method for embedded platform, which overcomes the problems of large network parameter quantity, slow detection speed and poor precision index in the prior art. The present application not only solves the problems of slow speed and low accuracy of the current detection method in the deployment of embedded devices, but also optimizes the computation graph for specific operators from the hardware level, enabling fast and accurate target detection on limited resource devices. The present application comprises the following steps: step 1: obtaining basic data and making data set; step 2: data enhancement preprocessing; step 3: constructing a benchmark network model and pre-training; step 4: building a lightweight detection network model; step 5: reparameterization operation and pruning on the overall network structure; step 6: knowledge distillation to restore accuracy; step 7: deployment and acceleration of embedded platform.

The application provides a photonic neural network system based on a multi-task neural network, which comprises: an optical computing module, taking a photonic artificial intelligence chip as a core, modulating an optical signal, and completing multiplication, summation and nonlinear operation of high-precision and high-speed optical analog quantities; a multi-task neural network module, having a main branch neural network and multiple secondary branch neural networks, and capable of simultaneously processing multiple classification tasks and regression tasks; and an optical computing communication and control module based on FPGA, connected with the optical computing module and the multi-task neural network module, and used for realizing real-time and high-speed communication of the optical computing module and the multi-task neural network module. The system has the characteristics of high bandwidth and low energy consumption, utilizes high-dimension parallel computing to greatly reduce the time of original neural network serial operation, has the advantages of large network scale and fast parallel operation speed, and can improve recognition accuracy and shorten training time on the basis of a single task.

In a transmission scene with large network delay or large out-of-order distance, in order to solve the problems that the bit width of a packet receiving bitmap of an RDMA data receiving end is too large and storage resources are occupied too much, the invention provides a packet receiving bitmap management method of out-of-order RDMA. According to the method, an RDMA queue does not apply for a storage space in advance for an annular packet receiving bitmap of Q), the annular packet receiving bitmap is segmented into a plurality of sub-bitmaps, and a sub-bitmap space is dynamically applied or released from an available sub-bitmap address pool according to needs; the available sub-bitmap address pool is a queue, all sub-bitmap addresses are queued into the address pool during initialization, when the QP needs a sub-bitmap, one sub-bitmap address is dequeued from the address pool, and after a certain sub-bitmap of the QP is used, the used sub-bitmap address is queued back into the address pool; when a certain sub-bitmap of the QP is 1, the sub-bitmap space can be released in advance. According to the invention, the RDMA data receiving end supports the packet receiving bitmap with a large bit width, the occupied bitmap storage resources are small, and the utilization rate is high.

The present application relates to the technical field of pavement crack detection, and particularly relates to a lightweight pavement crack detection method, wherein a lightweight feature extraction module SandGlass and a coordinated attention mechanism are integrated into an encoding-decoding structure to construct a lightweight pixel-level crack detection network, a pavement image database is used to train the network model, so that the network model can realize the same detection effect as a larger network model under less computing power, the coordinated attention mechanism module suitable for mobile terminals is introduced to reduce the influence of noise and improve the accuracy of crack detection, and finally the encoding-decoding structure is improved to fuse feature maps of different levels, so that the final prediction result is closer to the actual pavement condition. Further, the improved method can be effectively applied to mobile devices to realize real-time and effective end-to-end detection.

This application belongs to the field of interconnection network reliability and fault diagnosis technology, and discloses a method for intermittent fault diagnosis of low-bandwidth long interconnection networks based on graph attention mechanism. Targeting the hierarchical recursive structure and high connectivity of the network, under the PMC fault diagnosis model, a multi-round testing strategy is used to obtain test symptoms within the node's neighborhood. A feature vector is constructed for each node using local statistical feature extraction methods, and preprocessed using zero-padding and noise enhancement techniques. Finally, a graph attention network model is constructed, and the importance weights of neighboring node test results are dynamically learned using the attention mechanism to achieve accurate diagnosis of node fault states in the network. This application leverages the powerful local information aggregation capability of graph attention networks to overcome the diagnostic limitations of traditional algorithms, maintaining high diagnostic accuracy and robustness even with high fault rates, incomplete test symptoms, and large network sizes.

The present application provides a network topology configuration method, a communication system, and a related device. In the method, before distributed training of an AI model begins, a network controller can analyze a training task and determine traffic demand information for the training task. The traffic demand information comprises a bandwidth demand between different processors in a training process of an AI training task. Network topology information of an optical-electrical hybrid network can be determined on the basis of the traffic demand information. Configuration information of an optical switching device is then generated on the basis of the network topology information, so that the optical switching device can perform link switching on the basis of the configuration information and complete topology reconfiguration of the optical-electrical hybrid network. In this way, the network controller can actively perform traffic planning before AI model training, avoiding the problem of large network delay in the training process caused by a slow link switching speed of the optical switching device.

The invention relates to a media distribution method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps of detecting a network type of a terminal in response to a media acquisition request sent by the terminal for a target media; according to whether the network type of the terminal is a preset network type, sending the first index file or the second index file to the terminal; wherein the first index file is used for obtaining fragment links corresponding to all first media fragments of the target media, and the second index file is used for obtaining fragment links corresponding to all second media fragments of the target media; the duration of the first media fragment is less than that of the second media fragment. By adopting the method, the timeliness of code rate switching in the streaming media transmission process can be improved, and the phenomenon of media playing lagging under the condition of large network fluctuation is reduced.

The application provides an information transmission method and device, equipment and a non-transient readable storage medium. The information transmission method comprises the following steps: first information is directly sent to a network function device by calling a service of the network function device, the first information being application level information between a radio access network device and the network function device; and / or second information sent by the network function device by calling a service of the radio access network device is received, the second information being application level information between the network function device and the radio access network device. The present scheme can support direct information interaction between the radio access network device and the network function device through a service interface, avoid forwarding through an intermediate node, shorten a transmission path, reduce signaling routing delay and network overhead, and well solve the problems of large signaling routing delay and large network overhead in the prior art.

Provided in the embodiments of the present application are a method for determining a transport network layer address of a base station, and an apparatus, which are applied to base stations. The method comprises: acquiring transport network layer addresses associated with a target ground area; and when the base station operates in the target ground area, determining a target transport network layer address associated with the target ground area as the transport network layer address of the base station. The present invention solves the problem of large network overheads caused by excessive signaling interaction in non-terrestrial networks.

The application discloses a resource pushing method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: calling a first interaction prediction network to predict an interaction probability of a to-be-pushed object performing a target interaction on a to-be-pushed resource; determining a matching degree between the to-be-pushed object and the to-be-pushed resource based on the interaction probability; if the matching degree meets a pushing rule, pushing the to-be-pushed resource to the to-be-pushed object; the first interaction prediction network is trained based on a second interaction prediction network with a larger network scale, a first sample resource and a historical short-time interaction sequence related to the first sample resource; the second interaction prediction network is trained based on the first sample resource and a historical long-time interaction sequence related to the first sample resource, and the historical long-time interaction sequence comprises the historical short-time interaction sequence; the first interaction prediction network and the second interaction prediction network are both used for target interaction prediction; and the pushing accuracy and the pushing timeliness during resource pushing can be considered simultaneously.

The embodiment of the invention provides a wide-area computing power cascade network architecture based on hierarchical Dragonfly + Fat-Tree, and the network architecture comprises at least one computing power cluster which comprises a plurality of computing power chips in communication connection; the at least one first network architecture comprises a plurality of first nodes, and the first nodes in the same first network architecture are in communication connection to form a tree topology structure; the first-level autonomous architecture comprises a plurality of second nodes, the second nodes in the same first-level autonomous architecture are in communication connection to form a direct connection topological structure, and different first-level autonomous architectures belong to different areas; wherein at least one computing power cluster is in communication connection with a first-stage autonomous architecture through a first network architecture, and the first-stage autonomous architecture is connected with the computing power cluster in an area to which the first-stage autonomous architecture belongs. Therefore, according to the embodiment of the invention, a super-large-scale network architecture can be constructed.

The invention discloses a crane remote control image delay detection method based on machine learning, which belongs to the technical field of crane remote control, and comprises the following steps: S1, data acquisition and preprocessing; s2, feature extraction; s3, training a machine learning model; s4, carrying out real-time image delay detection; and S5, adjusting the control strategy. According to the method, a machine learning algorithm is utilized, complex features and time sequence relations in image data can be automatically learned, compared with a traditional method, image time delay can be more accurately detected, the detection precision is effectively improved, reliable data support is provided for remote control of the crane, and by continuously updating training data and optimizing a model, the accuracy of remote control of the crane is improved. The method can adapt to different network environments, crane operation scenes and working condition changes. Good detection performance can be kept no matter in an environment with large network fluctuation or in a complex operation scene, and the universality and practicability of the method are improved.

This invention discloses a 5G MIMO combined antenna and terminal. The 5G MIMO combined antenna includes a substrate, a first antenna element, a second antenna element, and an isolation slot. A conductive layer is disposed on one side of the substrate. The first and second antenna elements have identical structures and each includes a first radiating part, a second radiating part, a third radiating part, and a feed part. The isolation slot is disposed near the centerline of the conductive layer in the width direction. The first and second antenna elements are symmetrically arranged about the isolation slot. This 5G MIMO combined antenna supports all current 5G frequency bands (sub-6GHz bands) globally, except for millimeter wave bands. It has the advantages of flexible operating frequency bands and large network capacity. The antenna structure is simple and compact, with good isolation between array elements, and good antenna electrical performance within the operating frequency band.

To obtain a larger network capacity by setting three network hierarchies in a Clos network.SOLUTION: The network designed by the network design device 1 is provided with a plurality of first stage switches belonging to a first stage and connected to a plurality of terminals, a plurality of second stage switches belonging to a second stage and connected to the plurality of first stage switches, and a plurality of third stage switches belonging to a third stage and connected to the plurality of second stage switches. The network design device 1 includes a calculation unit 12 that calculates the number of terminals and the number of first stage switches so that the total probability of each blockage occurrence probability in a case where the number of terminals is increased by one from the maximum value of the number of terminals when the network is non-blocked does not exceed the allowable blockage rate ε and the network capacity is maximized in a network that satisfies the number of ports N of each switch, the total number a of a plurality of switches, and the terminal utilization rate p.SELECTED DRAWING: Figure 8

Devices, systems, methods, and processes for dynamically controlling power supplied from power supply units (PSUs) and battery units of network devices are described herein. Generally, network devices rely on redundant power supplies or large hold-up capacitors to address power supply issues. Redundant power supplies lead to sub-optimal efficiency, while hold-up capacitors lead to bulky network device taking up space within the PSUs. Therefore, the present disclosure describes disposing one or more battery units in linecard slots or PSU slots. The battery unit may provide redundancy for the power supply and can act as an effective filter for power signal fluctuations. The battery units and the PSUs are dynamically controlled based on a load demand associated with the network device and power supply sources connected to the network device. Thus, the battery unit actively participates in load sharing with the PSUs to operate the PSUs more efficiently.

The application discloses an edge computing method based on artificial intelligence, and particularly relates to the technical field of edge computing and artificial intelligence. The method comprises the following steps: deploying a light neural network model on an edge device to realize local data processing; dynamically optimizing the allocation strategy of a computing task between an edge node and a cloud end through a deep reinforcement learning algorithm; and realizing cross-device collaborative training by using an improved federated learning framework to guarantee data privacy. Through real-time data processing on the edge side, the average system response delay is reduced by more than 40%; the task offloading algorithm based on Q-learning can reduce network traffic by more than 30%; and the innovative differential privacy federated learning mechanism reduces the data leakage risk by 60% on the premise of ensuring model accuracy. Through multi-level technical cooperation, the application effectively solves the problems of high response delay, large network bandwidth occupation and insufficient data privacy protection in the traditional cloud computing architecture.

The application discloses a network loss optimization method considering source-load distribution balance degree, a memory and an electronic device, comprising an input layer, a calculation layer, a decision layer, a verification layer and an output layer, the input layer is used for inputting necessary parameters for optimization model calculation, including power distribution network framework present situation, load distribution, and related conditions of to-be-connected distributed photovoltaic; the calculation layer obtains network loss change rate under different distributed photovoltaic connection schemes by calculating input parameters, helps to select a connection scheme with the largest network loss reduction amplitude, and clearly defines the weak link of power distribution network loss problem; the decision layer selects a power distribution network loss optimization strategy according to the weak link of the power distribution network loss; the verification layer compares loss reduction benefits and optimization strategy investment costs to clearly define the feasibility of the optimization strategy scheme; and the output layer outputs an optimal distributed photovoltaic connection scheme and loss reduction optimization strategy, and realizes power distribution network loss optimization based on the influence of distributed photovoltaic connection and line load distribution balance degree on the power distribution network.

Embodiments of the present application provide a device control method and device, electronic equipment and storage medium, and relate to the technical field of Internet of Things. The device control method comprises: displaying at least one control entry for a virtual device, the virtual device being created by at least two smart devices allowing at least one same action to be performed; in response to a triggering operation on the control entry, generating a device control instruction, the device control instruction being used to instruct the virtual device to perform a set action corresponding to the control entry, the set action belonging to the actions allowed to be performed by the at least two smart devices creating the virtual device; and sending the device control instruction to the virtual device, so that the at least two smart devices creating the virtual device perform the set action synchronously in response to the device control instruction. The embodiments of the present application solve the problem of large network delay of multiple devices performing actions in the related art.

According to the lightweight time synchronization method and the performance monitoring method for the data center network, a request / response unicast communication mechanism is adopted, a slave clock initiates a time synchronization request actively, a master clock responds passively, and time synchronization is completed through data packet interaction, so that unicast negotiation is not needed, the time synchronization process is simplified, and the time synchronization efficiency is improved. The time synchronization protocol complexity is reduced, the network overhead and resource consumption are reduced, the time synchronization efficiency and reliability are further improved, convenient conditions are provided for data center network performance monitoring, the data center network performance monitoring capability is achieved, the master-slave clock synchronization performance is conveniently monitored, and the data center network performance monitoring efficiency is improved. Therefore, the technical problems that a traditional PTP time synchronization protocol is complex in protocol, low in efficiency, large in network overhead, insufficient in performance monitoring capacity and the like are solved, the time synchronization efficiency, reliability and precision are improved, and then the high-precision and high-reliability time synchronization requirements needed by a large data center network service system are met.

This invention relates to the field of metal processing equipment technology, and discloses a fully automatic edge trimming and milling integrated machine for processing large network metal cabinet shells. It includes a milling machine body, an edge trimming mechanism on one side of the milling machine body base, a support mechanism slidably connected to the upper surface of the milling machine body base, and a cleaning mechanism movably mounted at one end of the support mechanism. The edge trimming mechanism includes a linear telescopic component, a support frame, and a tool assembly. A receiving platform is integrally formed on one side of the milling machine body base, and the linear telescopic component is fixedly installed inside the receiving platform. This invention integrates the edge trimming mechanism onto the milling machine body, allowing the workpiece to complete the edge trimming and milling processes with only one clamping, avoiding positioning errors caused by multiple clampings. This significantly improves the processing accuracy of the cabinet shell and the relative position of the edges and holes. By adjusting the edge trimming position through the linear telescopic component, different edge trimming requirements can be met.

The embodiment of the invention provides a method and a device for determining a transmission network layer address of a base station, which are applied to the base station and comprise the following steps: acquiring the transmission network layer address associated with a target ground area; and when the base station runs to the target ground area, determining the target transmission network layer address associated with the target ground area as the transmission network layer address of the base station. According to the invention, the problem of large network overhead caused by much signaling interaction in a non-ground network is solved.

The invention discloses a multi-user multiplexing and routing method and system in a quantum key distribution network, and belongs to the technical field of quantum communication, and the method comprises the steps: 1, constructing a preparation link pool comprising a plurality of physical quantum links, defining a link in the pool, and maintaining a link ready state including polarization stability, a polarization reference value and a background QBER through periodic perturbation and measurement; step 2, allocating virtual identifiers to links in the prepared link pool, binding the virtual identifiers with the polarization reference value, executing a routing decision based on the virtual identifiers to generate a virtual identifier path, and executing state loading by querying the polarization reference value; and step 3, background type secret key generation is carried out on the link, and a secret key material is sliced into a virtual identifier. According to the invention, the response delay can be shortened, the resource allocation can be optimized, the service stability and the resource utilization rate under multi-user concurrent or burst requests can be improved, and the requirements of low-delay and high-reliability secure communication of a large QKD network can be met.

Approaches presented herein provide for the attestation of devices in managed networks, in order to verify state and establish trust in those devices as well as in the managed network and / or subnets. The devices in a network, including devices such as network switches, can perform self-attestation by transmitting attestation evidence using one or more network messages. One or more verifiers can verify the attestation evidence and determine which devices or subnets are trusted. Data and messages can then be routed along trusted paths through a trusted network or subnet, such that all devices along those paths are trusted devices. In order to reduce the volume of attestation traffic for large networks, a network device can provide attestation evidence to a verifier that is connected to that device, rather than propagating all evidence for all devices to a single verifier. Once verified, a list of trusted devices can be propagated rather than the instances of evidence that were used for the verification.

The application relates to the communication technical field, discloses a communication method, device and system, and is used for solving the problem of large network coding resource consumption in the prior art. The method comprises the following steps: a first node performs network coding on a plurality of carried data packets to obtain coded data packets, wherein the target node of the plurality of carried data packets is a second node; and the first node sends the coded data packets to the second node.

The invention relates to the field of intelligent traffic and computer vision, and particularly discloses a highway video event accurate analysis method and system based on edge calculation. According to the method, an event detection model and a multi-dimensional context fusion research and judgment mechanism are deployed at a roadside edge computing node, so that real-time analysis and local precise processing of a video stream are realized; meanwhile, the system combines a rule engine and a micro model to carry out cooperative verification and intelligent decision making on events, and the accuracy and reliability of event identification and classification are effectively improved. According to the invention, a bidirectional learning closed loop of continuous iteration of local optimization feedback and a cloud model is established, so that the system can adapt to traffic scene changes and evolve continuously. The system effectively overcomes the problems of large network delay, high bandwidth pressure, low precision of a front-end light computing scheme and many false alarms of a traditional cloud processing mode.

The present disclosure provides a method and device for limiting user terminal access to a UPF, a storage medium and an electronic device, and relates to the technical field of mobile communication. The method comprises: receiving a session establishment request forwarded by an SMF, wherein the session establishment request contains an identifier of a user terminal; querying whether the identifier of the user terminal exists in a whitelist of the UPF according to the session establishment request, wherein the whitelist contains identifiers of a plurality of user terminals stored in advance; and establishing a session between the UPF and the user terminal if the identifier exists. The present disclosure sinks the control point of user access to the edge UPF, which is dedicated to enterprise private networks, and limits user access by setting a whitelist in the edge UPF, thereby limiting user terminal access to the UPF while being applicable to SMFs in a large network position in a neighbor mode.

The invention provides a voice conversation method and device, equipment, a medium and a product, and relates to the technical field of big data. According to the method, global broadcast of the addressing matching message is realized by introducing the broadcast middleware, the dependence on middleware of a registration center is eliminated, and relatively large network overhead caused by frequent establishment and disconnection of HTTP short connection in related technologies is avoided. Besides, through cooperation of the first communication channel and the second communication channel, the processing logic of the robot reply text is generated based on the target text and the business data in combination with the target service node, so that low delay of voice data transmission and accuracy of business logic processing are ensured, interaction experience of the user in the voice practice process is also ensured, and the user experience is improved. The problems that node matching depends on registration center middleware and the network overhead is large in the prior art are effectively solved, and the applicability, the stability and the user satisfaction of the intelligent voice dialogue system in a distributed deployment scene are improved.