176 results about "Module network" patented technology

The invention discloses a network equipment dynamic data authority grading authentication management platform, and relates to the field of data authority. The platform comprises a network equipment position area sensing module, a network equipment multi-modal data analysis module, a network equipment compliance security authentication module, a multi-authority historical access behavior analysis module, a network equipment dynamic authority affirmation marking module and an access control execution module. The method comprises the following steps: performing multi-dimensional position secret-related grading according to a physical position, analyzing data content of network equipment in real time, identifying secret-related data flow of the network equipment in real time, performing multi-dimensional data secret-related grading, and performing compliance security authentication on the network equipment at the same time. According to the method, the confidential level of the network equipment is evaluated by combining a feedback mechanism of historical access behavior analysis of multi-authority personnel, a corresponding authority strategy is bound based on a position, data and equipment security ternary framework, and dynamic authority grading authentication is intelligently performed on the network equipment.

A container simulation network system based on a link autonomous agent is composed of a visual interaction module, a control plane module, a network simulation module and a resource management module. The modules perform information interaction and task collaboration through a uniform interface so as to realize automatic construction and management of the simulation network, a two-layer architecture design of network bridge containers and user containers is adopted, and each user container corresponds to one network bridge container; a link autonomous agent module is deployed in the network bridge container and is used for collecting a link state, calculating a performance adaptation degree and executing a scheduling operation bit; the visual interaction module realizes visual monitoring and interaction control of a simulation state; the control plane module is responsible for simulation scene analysis, link parameter generation and global scheduling management; and the network simulation module is responsible for calling a container generation tool to generate a specific container instance and carrying out resource monitoring and adjustment on the container.

The invention provides a description generation method based on continuous zero samples. The description generation method comprises the following steps: sequencing plain text training corpora according to the difficulty of continuous learning; setting that each training step t only can access the t-th corpus, and for the t-th learning step, generating a synthetic image for an input text of the t-th corpus by using a diffusion model; extracting image embedding of the synthesized image by using a CLIP image encoder; retrieving a group of text description embedding with similar semantics in a text corpus by using image embedding; image embedding and text description embedding are input into a fusion module network, and the output of a fusion module is defined as a soft prompt of a large language model; for an input text, key entities are extracted from the text through word frequency statistics so as to construct a soft prompt and a hard prompt of the large language model; hard prompts and soft prompts are input into a large language model network, and key parts in input are concerned through an attention mechanism.

The invention discloses a manufacturing industry production data real-time management system and method based on Internet of Things edge computing. Comprising a data acquisition and preprocessing module, a lightweight agent module, an edge computing module, a network transmission module, a platform management module, an application service module, a security protection module, a self-adaptive resource configuration module and a monitoring and diagnosis module. According to the invention, nearby acquisition, preliminary cleaning and real-time analysis of multi-source heterogeneous data from a sensor, a controller and industrial equipment are realized, the cloud burden is effectively reduced, and the overall processing efficiency is improved. A real-time anomaly detection mechanism based on a sliding window and machine learning is constructed, parameter deviation, equipment faults or process anomalies in the production process can be recognized within millisecond-level time, and early warning or control feedback is triggered.

The invention discloses a 6G-oriented cross-domain knowledge-driven network intelligent scheduling system and a 6G-oriented cross-domain knowledge-driven network intelligent scheduling method. The architecture comprises a cross-domain knowledge collaborative evolution module, a network closed-loop management module and a multi-normal-form learning decision module, the cross-domain knowledge collaborative evolution module is used for disassembling a network state into an environment domain, a network domain and a user behavior domain, and online incremental fusion of three-domain knowledge is realized through a graph neural network; dynamically updating the global knowledge base based on knowledge distillation; the network closed-loop management module is used for constructing a function closed loop of perception-reasoning-knowledge generation-decision issuing-verification optimization-memory retrieval; and the multi-normal-form learning decision module is used for fusing meta learning, reinforcement learning, federal learning and self-supervised learning to realize rapid migration and continuous evolution of the strategy. According to the method, the network data throughput, the fault recovery speed, the flow prediction precision, the resource fairness, the path efficiency and the task success rate are improved, and the method is suitable for complex 6G scenes such as air-ground integration and low-altitude traffic control.

The invention relates to the field of resource collaborative scheduling, and in particular relates to a resource scheduling method based on cloud-edge-end collaboration, which is used for solving the problems that the dynamic degree of resource allocation is insufficient and the computing efficiency is reduced due to static division of edge computing load bearing. The system specifically comprises a data privacy confirmation module, a path optimization accounting module, a network real-time monitoring module, a collaborative scheduling management module and a priority decision module. Preliminary data and calculation task distribution is performed through physical path distances of edge calculation nodes, load saturation judgment is performed according to task calculation requirements and calculation capabilities of the edge calculation nodes, calculation task transfer distribution is performed in combination with task time requirements, the load capacity of the multiple edge calculation nodes is better balanced, and the calculation efficiency is improved. And the edge computing node, the task load, the computing efficiency and the transmission efficiency are balanced.

The utility model discloses a data stream security gateway based on privacy computing, which comprises an Ethernet module, a network interface, a network transformer, a clock module and a power supply module, the network interface is connected with a primary coil of the network transformer, a secondary coil of the network transformer is connected with a communication interface of the Ethernet module, and the clock module is connected with the Ethernet module. The clock module is connected to a clock interface of the Ethernet module, and the power supply module is used for supplying power. According to the utility model, the network transformer is arranged between the Ethernet module and the network interface, interference signals are removed through the network transformer, and the use safety of the gateway can be ensured.

The utility model discloses a high definition interactive fusion type set top box, it includes PON module, DVB module, optical communication module, first interface module, network state indicating lamp, first memory, second interface module, power indicating lamp and second memory, PON module and DVB module pass through internal PHY communication circuit communication, integration in a equipment, optical communication module, first interface module, network state indicating lamp and first memory all are connected to PON module, and second interface module, power indicating lamp and second memory all are connected to DVB module, PON module adopts the chip of RTL9603C type and realizes, DVB module adopts the chip of MC2416 type and realizes, the high definition interactive fusion type set top box of the utility model, through with PON module and DVB module integration in a equipment, and together through internal PHY layer connection, has improved operating speed, has improved user experience and reduced user cost.

The invention discloses a phase modifier edge diagnosis system and method based on lightweight AI, relates to the technical field of power management, and aims to solve the technical problem of large automatic defect of current phase modifier diagnosis. Comprising a multi-mode sensing module, a lightweight AI edge deployment module, a network communication module, a data processing and storage module and an autonomous decision-making and fault diagnosis module, and the lightweight AI edge deployment module comprises an adaptive unit to avoid total model transmission. According to the system, significant technical gain is realized through the lightweight AI edge deployment module, the adaptive unit is embedded into the working condition feature mapping layer, model weight is dynamically adjusted in combination with working condition parameters such as the load rate of the phase modifier and the voltage of the power grid, misinformation caused by dirty data input is reduced, and the reliability of the system is improved. And the edge increment updating unit only transmits the deviation sample to perform local parameter updating, so that the diagnosis accuracy, the working condition adaptability and the operation efficiency are comprehensively improved, and efficient and reliable technical support is provided for the edge diagnosis of the phase modifier.

The application provides a kind of early warning method for monitoring abnormal state of gas pipe network, it is related to gas monitoring and early warning field, using the monitoring system including data preprocessing module, space-time feature module, ST-CGN network module, edge intelligent optimization module and result processing module;Among them, the ST-CGN network module outputs the multi-working condition probability vector corresponding to the sliding window slice through the input of three-dimensional feature tensor, at the same time, the space-time convolution layer in the ST-CGN network module simultaneously models the local dependence of space neighborhood relationship and time sliding window, maintains time causality;The edge intelligent optimization module realizes the edge optimization of ST-CGN network module through MAML fast adaptation, knowledge distillation, model pruning and quantization.This method effectively solves the problems existing in the existing gas pipe network monitoring method, such as data missing, easy to be disturbed by noise, insufficient multi-point coupling identification, poor cross-scene generalization ability, monitoring alarm with lagging nature and so on.

The invention discloses an off-line multi-scene collaboration and data synchronization mechanism and system based on a light guide film. State data of a user in multiple application scenes such as health monitoring, safety protection, social service, virtual world and smart home are subjected to local temporary storage, priority ranking and intelligent synchronization after network disconnection recovery through a unified identity authentication center. The unique identity feature code is locally cached after the user registers for the first time, and the user does not need to depend on online authentication in an offline state. The system comprises an off-line data temporary storage module, a priority ranking module, an off-network recovery synchronization module, a conflict arbitration module, a storage space management module and a manager off-line response module. When the network is interrupted, state change is temporarily stored in a local circular queue according to the priority (the highest life safety class), sequencing is carried out in real time during writing, and low-priority data is preferentially discarded when the space is full (coverage operation is executed at the head of the queue). And after the network is recovered, reporting according to a priority sequence, and reporting overtime retry by adopting a fragment transmission and breakpoint resume mechanism. The multi-device offline conflict is automatically arbitrated according to a version number, a timestamp and a device priority rule, and a conflict log retention period can be set. And when the supervisor is offline, automatically electing a temporary supervisor, and synchronizing data after recovery. According to the method, the problems that multi-scene state data are lost when the network is interrupted, off-line operation cannot be coordinated, and data conflicts are generated after recovery are solved, and a complete closed loop of online real-time synchronization, off-line temporary storage and supplementary transmission and high availability of a manager is realized.

The invention provides a converged communication gateway suitable for polar navigation. The converged communication gateway comprises a core control module, a 5G communication module, a multi-interface adaptation module, a network detection module, a data forwarding module, a position positioning module, a polar region signal enhancement module and a power supply module. The core control module pre-stores a maritime satellite coverage map, and automatically selects an active link according to the priority hierarchy of 5G priority, VSAT second priority and iridium bottom according to the real-time position and network state; and the differentiated cache duration is selected according to the switching type during switching, so that the data integrity is guaranteed. According to the invention, communication seamless switching in near-shore, medium-low-latitude ocean and high-latitude areas is realized, and communication continuity, adaptability and cost economy are guaranteed.

The utility model discloses a kind of intelligent cloud printers, it is related to printing equipment technical field, wherein, intelligent cloud printer includes shell, network module, printing module and data transmission interface module, main circuit board is equipped in shell, control module is equipped thereon.Network module, printing module, data transmission interface module are all in shell.Network module has signal light group and communication component, signal light group contains auxiliary circuit board and signal light, auxiliary circuit board is in the front side of shell and is electrically connected with control module.Printing module contains printing key and printing component, printing component is electrically connected with control module through main circuit board, printing key and signal light are exposed to the front side plate of shell and on auxiliary circuit board.Data transmission interface module contains flow SIM card plug-in interface and wired network interface, is set on the opposite side in the height direction of main circuit board.The technical scheme provided by the utility model can carry out wireless network connection and wired network connection simultaneously, and has the key setting of convenient and user operation.

The application discloses a billet detection method based on deep learning, and belongs to the technical field of billet detection, which is mainly used for the identification and positioning of billets in the process of discharging from a hot continuous rolling heating furnace. The multilayer feature extraction structure of the method is embedded with a CSA module, a SPPELAN network and an improved PANET network, the complex environment in which the billets are located is fully considered, end-to-end network construction from input of a detection picture to output of a prediction result is realized, and the problems of low precision and poor real-time performance of traditional methods are solved. Moreover, a residual network is used to extract image features, the network complexity and feature effectiveness are well balanced, and the model attention to the target and the prediction accuracy are improved. Meanwhile, the improved path aggregation network also makes the model pay more attention to billet features of different scales, and improves the detection precision. Therefore, the automation and intelligence degree of the method is high, and the industrial application prospect is good.

The utility model discloses a vehicle case for L4 level automatic driving car relates to AA technical field, including base, the top fixedly connected with box of base, the one side of box is provided with fan, the one side of box is provided with filter screen subassembly, and the inside of box is provided with machine case subassembly, the machine case subassembly includes battery module, and the one side of battery module is provided with network converter module. That is used for L4 level automatic driving car's vehicle case through using modularization design in the vehicle box, carries out independent encapsulation to each function module battery module, terminal module, network converter module, radar module, boost module etc.

The application relates to the technical field of network security management, and discloses a dynamic encryption network security management system based on federal learning, which comprises network data acquisition, federal learning processing, security state monitoring and the like. The network data acquisition module acquires traffic and encryption state data in real time, generates characteristic values and an encryption strategy dynamic adjustment set; the federal learning processing module generates individualized encryption strategies according to the traffic characteristic values; the security state monitoring module screens abnormal security data; the strategy matching module determines a target adjustment strategy through multidimensional matching; the strategy adjustment module drives an encryption engine to correct the strategy; and the strategy self-learning module updates a strategy correction factor based on feedback data. The system realizes dynamic encryption strategy adjustment and self-optimization, improves the intelligentization and self-adaptive capacity of network security protection, and is suitable for distributed network security management.

The invention relates to a flow control method of a new energy automobile, which comprises the following steps that: a channel management sub-module counts data flow of a network channel of an entertainment function module and a network channel of a data monitoring management module, and receives a command of the network management sub-module; performing opening, closing or rate limiting operation on a network channel of the entertainment function module, setting multiple levels of flow thresholds to realize elastic control on the network channel of the entertainment function module, and when the flow passing through the network channel of the entertainment function module reaches any level of preset threshold, performing rate limiting operation on the network channel of the entertainment function module. And the channel management sub-module sends a control signal to execute corresponding operation on the network channel of the entertainment function module. By setting the multi-level flow threshold value, refined flow control is carried out on the network channel of the entertainment function module, and the user experience is effectively improved.

This invention relates to an encrypted traffic detection method based on a NiN hybrid dense residual network. The method designs and creates a NiN hybrid dense residual network, and based on a sample dataset, analyzes the target traffic feature values ​​corresponding to encrypted traffic data packets. The NiN hybrid dense residual network is trained to obtain an encrypted traffic data packet recognition model, which is then used to identify and detect the encrypted traffic data packets to be analyzed. The invention also designs a corresponding system, incorporating modules for feature extraction, network training, and prediction / classification to efficiently achieve encrypted traffic data packet recognition and detection. The NiN hybrid dense residual network designed in this invention employs a multi-module network with progressive learning, resulting in higher model robustness and a lower false negative rate. It can uncover deeper features in encrypted traffic data, achieving efficient and stable data analysis and classification, and improving the efficiency of encrypted traffic recognition and detection.

This utility model discloses a connection terminal for multi-terminal collaborative control of an ERP system, relating to the field of multi-terminal collaborative control technology for ERP systems. The utility model includes a terminal body with an indicator light at the bottom corner and a touch screen embedded in the front. An extension surface with a built-in sensing area is located on the top of the terminal body. A mounting base is fixed to the back of the terminal body, with a wiring area on its upper part. The internal PCB board of the terminal body integrates a main control terminal module, a data interaction interface module, a network communication module, a user access control module, a local storage module, and a power management module. The touch screen is connected to the main control terminal module via a flexible cable. This utility model, by integrating the user access control module with the sensing area, supports contactless authentication methods such as mobile phone NFC recognition and IC card recognition. This not only improves system security but also makes access control more flexible and convenient.

The invention relates to an industrial data acquisition system, method and device based on an SD-WAN communication technology. The system comprises an edge layer module, a network physical layer module and a control layer module, the edge layer module comprises at least one edge layer; the control layer module comprises a data acquisition server unit, a first client device unit, a core switch unit, an SD-WAN controller unit, a firewall protection unit, an optical network unit and a mobile terminal APP unit. The network physical layer module comprises a transmission medium unit and a matched connection equipment unit; the edge layer module comprises a second client device unit, an edge switch unit, an edge data acquisition terminal unit and a device layer unit; and the management and control layer module is connected with the edge layer module through the network physical layer module. By adopting the system, the communication delay of the industrial data in the transmission process can be reduced, the data transmission stability is improved, and the data security is improved.

The utility model relates to portable intelligent electronic control test equipment for a new energy automobile. Any type of ECU (Electronic Control Unit) automobile electronic parts and electronic control units can be accessed to the test equipment as required to carry out data transmission and intelligent electronic control test operation. A CPU processor module U1 is in bidirectional connection with a CAN bus communication module U2, and the CPU processor module U1 is in parallel connection with an FRQ frequency supply module U3, a USB data transmission module U4, a PS power supply module U5 and an LAN network communication module U6. According to the invention, ECU electronic parts and electronic control units in an automobile can be accessed to the CAN bus communication module U2, instructions and fault information of each control of the ECU electronic parts and electronic control units in the automobile can be monitored conveniently and quickly, and all ECU electronic parts and electronic control units in the automobile can be connected, monitored and detected. The device is very convenient and practical.

The application discloses a high-precision encoder control system based on artificial intelligence, which comprises an information interconnection module, a blade posture prediction module and a high-precision control module. The information interconnection module is used for interconnecting information data between a plurality of groups of wind generator groups through a high-speed communication network. The blade posture prediction module is used for analyzing and predicting the optimal posture angle of the blades of the wind generator groups in real time. The high-precision control module is used for controlling the high-precision control of the blade posture of the target wind generator group according to the analysis result of the blade posture prediction module. The information interconnection module and the blade posture prediction module are network-connected. The blade posture prediction module and the high-precision control module are network-connected. The application has the characteristics of high control precision and strong practicability.

The present application relates to the technical field of speech conversion, in particular to a cross-lingual speech conversion method based on mutual information and SE attention mechanism, comprising a training stage and a conversion stage, the training stage comprising the following steps: first, introducing an SE module in the content encoder, through which the network can learn to obtain global information and improve the quality of the content representation generated by the content encoder; further introducing mutual information as a correlation measure to reduce the dependency between the content representation, speaker representation and fundamental frequency feature in an unsupervised manner, thereby achieving efficient disentanglement; finally, reconstructing the individual information in the target speaker sentence and the content information in the source speaker sentence in the decoder, thereby achieving high-quality cross-lingual speech conversion. At the same time, the method can also convert speakers not in the training set, i.e. complete the cross-lingual speech conversion of any speaker in an open set.

The utility model provides three operating system detection data processing equipment, including the casing cover and the power module, network switch module, three main control system modules, three hard disk extension modules and three communication extension modules that are all arranged in casing cover, the casing cover has the backboard for power distribution and data interaction between each module, and the power module, network switch module, each main control system module, each hard disk extension module and each communication extension module all take this backboard as the basic connection carrier, the power module is connected network switch module, each main control system module, each hard disk extension module and each communication extension module through the backplate, network switch module is connected with each main control system module through the backplate, and each main control system module is connected with a hard disk extension module and a communication extension module respectively through the backplate, and the multi -source data parallel processing is realized through the construction three operating system framework, and the space burden and power consumption burden of the application scene such as railway detection vehicle are reduced.

A status notification method includes: determining a lock status of a logical lane in a first optical module, where the first optical module belongs to a first network device, and the lock status includes a locked state or an unlocked state; and sending uplink notification information when the lock status indicates that the logical lane in the first optical module is locked, where the uplink notification information is used to enable a second optical module to determine that the logical lane in the first optical module is locked, the second optical module belongs to a second network device, and the first optical module is coupled to the second optical module.

This invention discloses an intelligent mental health counseling robot and its service system, comprising a robot body, a robot head, a central processing unit, a voice recognition module, a network signal receiving module, a camera module, and a display module. The robot head is fixedly connected to the upper end of the robot body, and the central processing unit is installed inside the robot head. The system also includes: a water tank installed on the upper end of the robot head, with a delivery pipe installed on the side of the water tank and a start / stop valve fixedly connected to the delivery pipe; and a drain outlet located inside a transfer block, with a blocking rod installed inside the drain outlet. This intelligent mental health counseling robot and its service system can effectively alleviate user fatigue during psychological counseling, preventing users from becoming more agitated after prolonged counseling, and also preventing the robot from overheating during long-term operation.

The invention discloses an intelligent office resource dynamic prediction and scheduling system based on machine learning. Comprising an employee concentration collection module, a task information analysis module, an equipment hidden loss monitoring module, a network load detection module, a demand priority calculation module, a resource matching degree calculation module, a dynamic adjustment module, a scheduling execution module and a machine learning optimization module, and all the modules establish a real-time data interaction mechanism. The weight coefficient is dynamically optimized through multi-module cooperation and machine learning, and accurate scheduling of office resources is achieved. According to the system, the problem that existing scheduling only depends on task urgency and does not consider multiple key factors is solved, the resource scheduling adaptability and stability are improved, and the dynamic requirements of smart office are met.

A video online recommendation system combined with network information comprises a video quality evaluation module, a network measurement module and a multi-target reordering module; the recommendation algorithm of the video quality evaluation module selects candidate videos; the network measurement module measures the network condition during the request of the video; the multi-target reordering module estimates the residual transmission time of the candidate videos according to the network bandwidth feedback calculated by the network measurement module, reorders the candidate video list selected by the recommendation algorithm, and selects a preset number of video combinations to transmit to the user end. The present application proposes a new short video recommendation system and method, which takes network bandwidth into the video recommendation system. The system can select the video combination that can be successfully transmitted within a specified time and bring the highest actual income. In the case of poor network bandwidth, compared with the traditional recommendation mechanism, the system can improve the video playing time by 160%, that is, the income is improved.

The invention relates to the field of information management, in particular to a gynaecology and obstetrics nursing information management method and system. The gynaecology and obstetrics nursing information management system comprises a gynaecology and obstetrics nursing information acquisition module, a network environment data acquisition module, an encryption strategy selection module, a gynaecology and obstetrics nursing information encryption transmission module and a real-time training module. According to the method, the corresponding encryption strategy is quickly selected for the current network environment through the encryption strategy selection model, and then the gynecological nursing information is encrypted and transmitted through the selected encryption strategy, so that the network transmission performance and the resource utilization rate in the network transmission process can be improved on the basis of ensuring the transmission security; and the encryption strategy selection model is trained in real time through reinforcement learning, so that the encryption strategy selection model can better fit an actual network environment.

The invention provides a power system insulator YOLOv7 prediction model and a training method thereof, and relates to the technical field of insulator detection, the power system insulator YOLOv7 prediction model comprises a backbone network, the backbone network comprises a plurality of CBS modules and an ELAN network model which are connected in sequence, and the ELAN network model is used for processing feature maps output by the plurality of CBS modules; the MCIE module is connected with the ELAN network model and used for extracting feature information of different scales from a feature map output based on the ELAN network model and fusing the feature information of different scales; and a CG-LA module. According to the method, the MCIE module and the CG-LA module are introduced into the backbone network, so that the ability of the model to extract information from feature maps of different scales is improved, context information is integrated, the understanding of the model on a target and scene relationship can be enhanced, focusing on key features is realized, the detection ability is improved, and the detection efficiency is improved. The problems that the insulator detection environment is complex and is easily interfered are solved.