663 results about "Module network" patented technology

A small-size vehicle detection deep learning model based on feature fusion of multi-scale modules is provided, which solves the problem of small-size vehicle image detection. The model includes a Backbone network, a Neck layer and a Head network, wherein a C2f_DCNv3 module based on the combination of deformable convolution v3 (DCNv3) and a cross stage feature fusion (C2f) module and an SPPF_LSKA module based on the combination of a spatial pyramid pooling fast (SPPF) layer and a large separable kernel attention (LSKA) module are introduced into the Backbone network; a C2f_SCConv module based on the combination of spatial and channel reconstruction convolution (SCConv) and a C2f module is introduced into the Neck layer; and a multi-scale kernel detection (MSK_Detect) module is introduced into the Head network.

The invention relates to the technical field of information security, in particular to a power distribution Internet of Things cloud side end collaborative interaction security protection system, which comprises a network flow monitoring module, an abnormal flow identification module, a security threat response module, a network attack early warning module, a behavior pattern analysis module and an access authority management module. According to the method, the network flow data is collected and analyzed in real time, the abnormal flow recognition speed and accuracy are enhanced, the security threat response speed and efficiency are improved, measures are taken in time at the early stage of a problem, the risks of data leakage and network attacks are reduced, and the timeliness of defense measures is improved by combining security threat response and network attack early warning; by analyzing the relevance between threat events, the complex multi-stage attack activity is effectively predicted and identified, the access data and the communication mode of the equipment are monitored in real time, and the access authority and the trust level are adjusted, so that the security protection is more targeted and flexible, and the management efficiency of the risk equipment is improved.

The invention relates to the technical field of intelligent security operation and maintenance, in particular to an artificial intelligence network security system based on multi-modal large model training, which comprises a server fault diagnosis module, a network attack detection module, an endpoint security monitoring module, a key management optimization module and a threat analysis feedback module. According to the method, the fault prediction accuracy is improved through multi-dimensional data analysis, service interruption caused by sudden hardware faults is reduced, the network access frequency, source and instruction features are evaluated based on the server abnormality, the attack detection accuracy is improved, the misjudgment risk is reduced, the endpoint equipment execution behavior, resource calling and behavior sequence are extracted, and the service performance of the terminal equipment is improved. Fine-grained security monitoring is realized, attack traceability is enhanced, a key strategy is dynamically adjusted, security adaptability is improved, strategy lag risk is reduced, multi-level data is integrated to calculate threat behavior and attack fitting degree, threat assessment fineness and response speed are enhanced, and global security situation awareness is improved.

The invention relates to the technical field of power distribution cabinet detection, and discloses an intelligent detection system and method for a high-low voltage power distribution cabinet, and the system comprises a data collection module, a feature extraction module, a network construction and updating module, a fault positioning module, a positioning map generation module, and a self-healing control module. The method comprises the following steps: generating a multi-physical field coupling data set by synchronously collecting electromagnetic radiation spectrum and cabinet vibration waveform, performing time-frequency domain conjoint analysis to extract fault feature vectors, constructing and dynamically updating a fault propagation network, identifying key fault sources by combining betweenness centrality and historical case matching, and generating a fault positioning map; accurate self-healing control is realized by applying reverse electromagnetic pulse and adjusting contact pressure based on the atlas; according to the invention, the problems of low positioning precision and mechanical self-healing control means during detection of the high-low voltage power distribution cabinet are solved, and the fault detection efficiency and reliability are significantly improved.

The invention discloses an AI oT-based cloud network side-end federated learning system, and relates to the technical field of cloud network side-end federated learning, comprising the combination of Internet of Things, cloud computing and artificial intelligence technology, and through intelligent cooperative computing of cloud-side-end three-layer computing resources, the cloud network side-end federated learning is realized. The AI oT-based cloud network side-end federated learning system further comprises a cloud module, a network module, an edge module and a terminal module, and has the advantages that local model aggregation is firstly carried out on edge nodes, a part of calculation tasks can be completed at an edge end, a model result subjected to primary processing and aggregation is uploaded to the cloud, and the cloud network side-end federated learning is realized. Different edge nodes and terminal devices have different computing capabilities, the cloud end dynamically distributes training tasks to match tasks with different complexity degrees with devices with corresponding computing capabilities, and the edge nodes with strong computing capabilities can undertake more complex model training sub-tasks. And the terminal equipment with relatively weak computing power is responsible for some simple tasks such as data preprocessing and the like.

The invention discloses a security encryption transmission system for game data, and relates to the technical field of game data encryption. Comprising a network environment data collection module, a network risk feature extraction module, a feature analysis and machine learning evaluation module, a network environment classification module, a low-risk network Hash algorithm module and a high-risk network Hash algorithm adjustment module, and various parameter information of the current network environment is comprehensively collected. According to the method, the network environment is intelligently evaluated through machine learning, the system recognizes a high-risk situation in real time, the Hash algorithm is dynamically adjusted, stronger encryption is adopted when the risk is high, the communication safety is improved, and viruses and ransomware are prevented. And when the risk is low, a fixed Hash algorithm is used, and high performance is kept. According to the mechanism, data security is effectively guaranteed, resource use is optimized, and security and system performance are balanced.

The invention relates to an LLM-Agent-based transformer substation TSN intelligent scheduling method and system. The system comprises an RAG-based TSN knowledge base management module, a TSN network global situation awareness module, an LLM-RAG-based TSN intelligent scheduling decision module, an LLM-based reflection module and a dynamic TSN scheduling simulation verification module. The RAG-based TSN knowledge base management module is used for constructing a knowledge base comprising a TSN scheduling algorithm, a TSN scheduling rule, a TSN protocol and a TSN network topology structure; the TSN global situation awareness module provides real-time data support for intelligent scheduling decision making; the LLM-RAG-based TSN intelligent scheduling decision-making module is used for carrying out intelligent scheduling decision-making by utilizing an LLM-RAG technology; the LLM-based reflection module ensures the rationality and effectiveness of a scheduling result; and the dynamic TSN scheduling simulation verification module performs simulation verification on the TSN scheduling result optimized by the LLM-based reflection module, so that intelligent scheduling of the transformer substation TSN network is realized, manual intervention is greatly reduced, and manpower cost investment is effectively reduced.

The invention discloses a network communication dynamic optimization method based on multi-module collaboration, and relates to the technical field of network communication, a dual-mode communication module is deployed at each network node, and the network communication dynamic optimization method comprises the following steps: each network node broadcasts own existence information and power line channel characteristics through an HPLC (High Performance Liquid Chromatography) channel of the dual-mode communication module; each network node scans surrounding wireless networks through an HRF channel of the dual-mode communication module and reports own wireless channel quality information to the gateway; the global state sensing module collects all information and constructs a global network view containing physical topology and a channel quality map. A dual-mode cooperation mechanism, a machine learning prediction model and a dynamic decision strategy can adapt to complex dynamic environments such as power line noise fluctuation and wireless interference change, and communication parameters can be autonomously optimized without manual intervention; and meanwhile, the modular design is convenient to expand to a multi-mode communication scene, and has a wide application prospect.

The invention provides a light guide plate surface defect detection method based on a GCC-YOLOv8 network model. The method comprises the following steps: acquiring an image of a light guide plate to be detected; the image is input into a GCC-YOLOv8 network model for defect detection; outputting surface defect information of the light guide plate based on a detection result; wherein the GCC-YOLOv8 network model is constructed based on a YOLOv8 network model, and comprises a backbone network unit, a neck network unit and a detection head network unit with three parallel detection branches, which are arranged in sequence; two GKA-C2f1 modules are arranged at the tail end of the backbone network unit; the neck network unit comprises an FPN network unit and a PAN network unit; the FPN network unit comprises two up-sampling modules, two C2f2 modules, two Concat modules, an SPPF module and a CPCA module; the PAN network unit comprises two CBS modules, two Concat modules, two C2f2 modules, three CPCA modules, a CFC module and an SFC module. The light guide plate surface defect detection method has good light guide plate surface defect detection performance and robustness, and also has good detection speed.

The invention discloses a high-reliability precise time service device, and particularly relates to the field of precise time service, and the device comprises a GNSS antenna, a Beidou satellite receiver, a main control module, a rubidium atomic frequency standard module, a frequency distribution module, a time code generation module, a network time service module, a display control module and a power module. Standard time signals are obtained through the Beidou satellite receiver, time calibration and taming are carried out through the rubidium atomic frequency standard module, and it is ensured that the output time signals and standard time are kept synchronous; the frequency distribution module distributes and amplifies a 10MHz standard frequency signal output by the rubidium clock and then divides the 10MHz standard frequency signal into multiple paths for output; the time code generation module completes 1pps and B code signals; the network time service module provides standard time information generated by the equipment to a computer with a time service requirement in a network by using an NTP standard network time service protocol; and the power supply module provides conversion from 220V to direct current through AC / DC conversion, provides power supply for each functional module and controls the power-on sequence.

The invention discloses a semi-supervised medical image segmentation method based on suspicious pixel mutual correction. The method comprises the following steps: 1, preprocessing and dividing a data set; 2, a semi-supervised medical image segmentation network model SPMC is constructed and comprises a network A, a network B, a CDPS module and an SPLAT module, and the network A and the network B adopt the same encoder and decoders with different up-sampling modes; the CDPS screens annotation data prediction results output by the network A and the network B, identifies common trapping pixels which are difficult to predict accurately, and corrects the common trapping pixels by referring to existing tags; the SPLAT screens unlabeled data prediction results output by the network A and the network B, performs pseudo supervision on suspicious pixels in the network B by using credible pixels generated by the network A, calculates total loss and updates parameters; 3, training a network model SPMC; 4, evaluating SPMC performance and optimizing parameters; and 5, inputting a medical image to be segmented into the trained SPMC, and outputting a segmentation result. According to the invention, the prediction accuracy of complex and fuzzy difficult areas in the image is improved.

The invention discloses a network security threat detection system based on artificial intelligence. The system comprises a network security data acquisition module, a network security data optimization module, a network security threat prediction model establishment module, a model optimization module and a network security threat intelligent detection module. The invention relates to the technical field of network security data processing, in particular to a network security threat detection system based on artificial intelligence, which innovatively introduces weighted distance measurement driven by field correlation, adaptive neighbor number selection and a distance deviation threshold control strategy. A K-nearest neighbor method is improved to realize data completion, and the accuracy of a network security threat detection result is improved; a double-layer long-short-term memory network and self-adaptive threat threshold calculation are provided, and high-precision threat prediction of network security is realized; a sine smooth attenuation inertia weight strategy and a sine modulation contraction factor strategy are introduced to improve a particle swarm optimization algorithm, and the prediction accuracy and stability of the model are improved.

The invention discloses a fire detection and early warning method and system based on improved YOLOv8, and belongs to the technical field of image recognition, and the method comprises the steps: building a fire data set, marking the fire data set, and dividing the fire data set into a training set, a verification set and a test set; the YOLOv8 network is improved; the original YOLOv8 network and the improved YOLOv8 network are trained, evaluated, adjusted and tested, and an optimal model is determined; deploying the optimal model to an edge AI development board of a ground station; a real-time video stream of the unmanned aerial vehicle is obtained through the unmanned aerial vehicle camera and is transmitted to the edge AI development board for fire detection; a fire signal is detected by using the edge AI development board, and early warning is carried out through the fire alarm module; the system comprises a fire data set establishment and processing module, a YOLOv8 network improvement module, a YOLOv8 network training module, an optimal model deployment module, a fire detection module and a fire early warning module. The method has the technical advantages of high precision and light weight.

The invention relates to the field of industrial internet safety monitoring, and particularly discloses an industrial internet safety monitoring control system which comprises a network safety monitoring module, a control logic monitoring module, a traceability analysis module and an early warning execution module. The network security monitoring module analyzes a network attack risk level and an illegal access risk level through traffic baseline comparison, attack feature matching and risk model assessment; the control logic monitoring module identifies control logic abnormity based on the equipment operation state data and triggers a verification alarm; the traceability analysis module adopts a decision tree algorithm, combines the operation log, the behavior track and the version updating record, and judges that the tampering reason is misoperation, malicious tampering or system updating; and the early warning execution module triggers differentiated grading early warning according to the risk grade and tampering reason classification. The system can effectively identify external attacks, illegal access and logic tampering behaviors, and the security protection capability of the industrial internet is improved.

The invention relates to the technical field of network security analysis, in particular to a big data network signal security early warning system. The system comprises a network signal big data stream transmission module, a network signal ratio difference detection module, a network signal division result safety early warning module and a network signal abnormity safety response module. Data acquisition equipment can be deployed at an entrance and an exit of each network equipment, and network signal data real-time acquisition and big data stream type transmission conversion can be carried out, so that a network signal big data stream can be obtained; carrying out time sequence flow size and access frequency statistical analysis on the network signal big data flow, and carrying out network signal ratio difference detection division and abnormal safety early warning processing at the same time to generate a network signal abnormal safety early warning signal; and uploading the network signal abnormity safety early warning signal to a cloud platform to carry out abnormity safety response processing so as to generate a network signal abnormity safety response protection strategy. According to the invention, network signal abnormity can be efficiently monitored in real time and early warning can be provided.

The invention discloses an intelligent computer network information security control system, and particularly relates to the technical field of network information security control, and the system comprises a terminal equipment security evaluation module, an access control module, a network state control module, a backup data security control module, an early warning display module and a database. According to the method, the security level of the terminal equipment is comprehensively evaluated by acquiring each parameter of the terminal equipment, and password verification strategies of different levels are set according to the security level of the terminal equipment, so that the protection capability of the terminal equipment with low security level is enhanced, the influence of malicious terminal equipment on a hospital patient information server is effectively avoided, and the safety of the hospital patient information server is improved. And the convenience of login verification of the high-security-level terminal equipment is improved. The security level of the backup data of the hospital patient information server is analyzed, the response measures corresponding to the security level of the backup data are implemented, and the security and continuity of the backup data are ensured.

The invention provides a gas abnormal working condition monitoring and alarming system, relates to the field of gas monitoring and early warning, and adopts a monitoring system comprising a data preprocessing module, a spatial-temporal characteristic module, an ST-CGN network module, an edge intelligent optimization module and a result processing module. Wherein the data preprocessing module comprises a sliding window fragmentation method, a missing value interpolation method, a noise filtering method, a normalization method and a coding method; the spatial-temporal feature module extracts spatial correlation features, time dynamics features and causal propagation features, and fuses and splices the features into a three-dimensional feature tensor; the ST-CGN network module outputs a multi-working-condition probability vector corresponding to a sliding window fragment through the input of a three-dimensional feature tensor; and the edge intelligent optimization module realizes edge optimization of the ST-CGN network module. The system is used for monitoring and early warning gas abnormal working conditions, solves many problems existing in traditional gas pipe network monitoring, and has the advantages of being rapid in response, high in monitoring precision, high in cross-scene generalization ability and the like.

The invention discloses an industrial big data platform system for intelligent supervision, which comprises a management control module, an analysis processing module and a transmission storage module, and is characterized in that the management control module is in network connection with the analysis processing module, and the analysis processing module is in data connection with the transmission storage module; the management control module is used for carrying out login management control on an industrial control system, the analysis processing module is used for carrying out analysis control processing on industrial supervision, and the transmission storage module is used for carrying out transmission storage management on data information. The management control module comprises a login management module, a mutual authentication module and a bill confirmation module, the login management module is in data connection with the mutual authentication module, the mutual authentication module is in network connection with the bill confirmation module, and the login management module is used for managing and controlling user login. The system has the characteristics of efficient management control and high safety.

The invention discloses a network protocol analysis system based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an MCP module, a network analysis module, a data source module, a context module, an MCP interface, a data source interface and a network analysis interface. An MCP module, a network analysis module, a data source module and a context module. All the modules work cooperatively through standardized interfaces to form a unified network protocol analysis platform. The system supports a context-driven tool calling process and a double-channel execution structure, control logic and data transmission are decoupled, and efficiency and expandability in a large-size Pcap data processing scene are improved. Through a unified semantic modeling and context state tracking mechanism, through analysis of a multi-layer protocol, multi-round traceable execution of a task and structured expression of an analysis result are realized, and infrastructure support is provided for a modularized, pluggable and reusable intelligent network protocol analysis platform.

The invention discloses a vehicle-mounted intelligent system module network card establishing method, device and equipment and a storage medium, and relates to the technical field of automobile communication, and the vehicle-mounted intelligent system module network card establishing method comprises the steps: sending dialing data to a target communication base station, so that the target communication base station feeds back an initial callback signal; obtaining the initial callback signal, and determining an establishment state of a network card according to the initial callback signal; and when the establishment state is successful, analyzing the target domain name system to realize network access, and completing the establishment of the network card. According to the invention, by accurately controlling the dialing process and domain name resolution, the stability and reliability of vehicle network connection are significantly improved, and the network performance of the vehicle is optimized.

The invention discloses an intelligent livestock necklace system with a self-adaptive adjustment function and a control method. The system comprises a behavior perception control module, a network perception transmission module, a group cooperation module, a strategy optimization module and an energy management module.

The invention relates to the technical field of communication scheduling command, in particular to a mining converged communication scheduling command system which comprises an information acquisition and indication module, an energy sensing communication control module, a network self-healing management module and a command scheduling integration module. According to the invention, the key information of the mining area is collected in real time and comprehensively processed, so that the accuracy and practicability of data are greatly improved, a mining area manager can quickly respond, the safety management efficiency of the mining area is improved, and the signal power and coding complexity of communication equipment are dynamically adjusted according to the environment and equipment energy state; the stability and efficiency of communication are obviously enhanced, rapid change of the internal environment of the mining area can be flexibly adapted, the self-healing network can rapidly reconstruct the network when finding a link quality problem, the continuity of communication is ensured, the fault recovery speed and reliability are improved, emergency response and resource configuration are optimized, and the network reliability is improved. The rescue efficiency of the mining area and the protection of life and property are enhanced.

The invention discloses a high-voltage SVG adaptive voltage control method based on deep learning, and belongs to the technical field of power equipment, and the method comprises the following steps: S1, multi-source data collection and preprocessing: the S1, multi-source data collection and preprocessing comprises a data collection module and a data preprocessing module, the data acquisition module comprises a high-precision sensor network and acquires various data in real time; the data preprocessing module comprises missing value filling through a sliding window method, wavelet transform denoising, multi-dimensional feature proof construction and normalization processing; s2, constructing a deep learning model, wherein the construction of the deep learning model in S2 comprises a model framework module, a network parameter module and a training strategy module. According to the method, the nonlinear characteristics of the power grid are captured through the deep learning model, the method can adapt to dynamic scenes such as power grid topological structure sudden change and new energy output intermittence in real time, and in the power grid with the new energy permeability exceeding 30%, the voltage regulation error can be reduced to + / -0.5% and is remarkably superior to + / -2% of a traditional method.

Certain aspects of the present disclosure provide techniques for a UE with multiple universal subscription identity modules (i.e., a multi-USIM device) or concurrent radio access technology (C-RAT) capabilities to receive and respond to a page from a network of a first USIIM of the UE while not activating a receiver on that network by receiving the page (from the network of the first USIM) on a receiver active on a network of a second USIM. A method that may be performed by a user equipment (UE) includes sending a request to register for paging events in a registration request message to an access and management function (AMF) of a network of a first universal subscriber identifier module (USIM) of the UE; sending a paging public user identifier (PPUID) of the UE, in a protocol data unit (PDU) session, to a paging server via a network of a second USIM of the UE, wherein the paging server is associated with the network of the first USIM; and receiving a message associated with the first USIM via the PDU session or another PDU session via the network of the second USIM.

The invention provides an optical module, network equipment and a data transmission method, and belongs to the field of optical communication. A FlexE interface is arranged in the optical module, after a FlexE code block stream from a system side is converted into a plurality of paths of Ethernet code block streams by utilizing a FlexE channelization characteristic, an optical signal is generated after electro-optical conversion, and then the optical signal is sent out, so that one interface, butted with network equipment, of the optical module is mapped into a plurality of optical interfaces, the optical interface density is improved, and the optical communication efficiency is improved. And hashing of a high-density optical interface can be realized.

The invention provides a coal mine equipment network operation and maintenance management system and method based on 3DMineGlS, and particularly relates to the technical field of coal mine network operation and maintenance management, and the system comprises a data access module, a network topology visualization module, an equipment state monitoring module, a flow monitoring and analysis module, and an alarm and fault recording module. According to the system, basic information of coal mine equipment is collected through the data access module, and three-dimensional visual display is achieved through the network topology visualization module. The equipment state monitoring module monitors the running state of equipment in real time, and the flow monitoring and analyzing module monitors and analyzes flow data. And the alarm and fault recording module triggers alarm when the equipment fails or the flow is abnormal, and records related information for subsequent query and analysis. The technical problems of original equipment access mode, inaccurate fault positioning and insufficient visualization degree in the operation and maintenance management platform of the coal mine network system are solved, the operation and maintenance management efficiency of the coal mine network system is improved, and the operation and maintenance cost and difficulty are reduced.

The invention discloses a smart park electricity consumption information acquisition equipment warehouse logistics remote monitoring system, relates to Internet of Things remote data monitoring, and solves the problems that in the prior art, the electricity consumption information acquisition equipment warehouse logistics remote monitoring capability is lagged, the data communication efficiency is low, and the data processing capability is poor. The system comprises an intelligent sensor module, a video monitoring module, a central server module, a remote control module, a network security module, an artificial intelligence module, a risk early warning module, a system integration and interconnection module and a privacy protection module. The remote control module provides a remote control function through the logistics monitoring cloud platform, and the logistics monitoring cloud platform remotely monitors and controls the electric information acquisition equipment and the warehouse logistics equipment through a remote communication protocol and a security access method; according to the invention, the warehouse logistics remote monitoring capability of the smart park electricity consumption information acquisition equipment is greatly improved.

The embodiment of the invention provides an optical module, a network device, a communication system and a co-cable detection method, and the method comprises the steps: enabling a receiving end device to determine the position feature of a reflection point based on an electric signal generated by a received optical signal through transmitting an optical pulse signal with a preset code pattern by the optical module as a transmitting end; and whether the optical fiber links share the same cable or not can be conveniently detected based on the position characteristics of the reflection points of different optical fiber links. The same-cable detection is carried out no longer depending on the OTDR, but the receiving end equipment undertakes the task of determining the position characteristics of the reflection points, so that the limitation of the OTDR technology in application is reduced to a certain extent; therefore, the optical module serving as the transmitting end does not need to be provided with various additional devices for processing the reflected signals such as separating the reflected signals and sampling the reflected signals in order to realize the OTDR, and the power consumption of the whole optical module caused by the additional devices in the optical module of the transmitting end in the OTDR technology is reduced.

The present application relates to the field of data processing, and disclosed are a network card device and a data transmission system, which are used for solving the problem whereby data transmission occupies the access bandwidth of a host memory. The network card device comprises a storage module; a descriptor queue address register; a calculation high-speed link hard core module, which is used for transmitting data sent by a host to a first data storage area, transmitting a descriptor sent by the host to a first descriptor storage area, and transmitting a trigger instruction sent by the host to a network data processing module; and the network data processing module, which is used for acquiring a base address from the descriptor queue address register when receiving the trigger instruction, reading a target descriptor in the first descriptor storage area based on the base address and the trigger instruction, reading target data corresponding to the target descriptor in the first data storage area, and outputting the target data to a network. The present application can reserve the access bandwidth of a host memory for other functions running on the host to use, so that the overall operation efficiency of the host is improved.

The present application relates to the technical field of terminal devices. Provided are a network state detection method, a terminal device, and a computer-readable storage medium. By means of the present application, network state detection can be implemented on the basis of a baseband processor and an application processor of a terminal device, wherein the baseband processor may report, to a network detection module in the application processor, a caching duration of a data message in a cache queue, and after receiving the caching duration of the data message, which caching duration is reported by the baseband processor, the network detection module may determine an uplink network state of the terminal device according to the caching duration of the data message, which caching duration is reported by the baseband processor. In the present application, a caching situation of a data message in a baseband processor is used to dynamically determine a network state, such that not only can the network quality be obtained in a timely manner, but the accuracy of the network state can also be ensured.