324 results about "Module network" patented technology

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 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 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 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 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.

The invention discloses a digital power equipment management system which comprises an equipment automatic management module, a network topology visualization module, an intelligent alarm strategy engine, an automatic inspection execution module, a work order closed-loop management module, a multi-dimensional safety management and control module, an asset full-life-cycle data center and an AI fault prediction module. According to the invention, automatic discovery and full-life-cycle intelligent management of equipment can be realized, network topology dynamic visualization and fault rapid positioning capabilities are improved, a false alarm rate and operation and maintenance cost are reduced through triple verification intelligent alarm and adaptive routing inspection, a standardized operation and maintenance system is constructed in combination with a work order closed-loop process and multi-dimensional safety management and control, and the operation and maintenance efficiency is improved. And potential faults are warned in advance by using an AI prediction technology, so that the intelligent level and the operation reliability of power equipment management are comprehensively improved.

Disclosed in the present disclosure is a fast evaluation method of site seismic liquefaction hazard based on artificial intelligence algorithm: establishing a historical seismic and site information database, the database including a demand input module, a web crawler module, a data processing module, and a database module connected in sequence; a neural network model performs prediction to acquire a post-earthquake site dominant frequency; and, on the basis of the post-earthquake site dominant frequency, acquiring a site earthquake damage degree and seismic performance parameters. The present disclosure solves the problem of fast evaluating post-earthquake site earthquake damage and site seismic performance parameters, and can rapidly evaluate the site liquefaction or softening earthquake damage degree and site seismic performance parameters in given earthquake conditions.

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 discloses an aviation radiation dose global distribution real-time calculation system based on ground detection equipment, which belongs to the technical field of radiation detection and comprises a ground radiation detection module, a dose simulation module and a data correction module. The ground radiation detection module is used for continuously collecting ionizing radiation data of an atmospheric bottom layer in a calm state and a solar high-energy particle event so as to improve data precision and a signal-to-noise ratio under a weak dose, and the dose simulation module receives latitude and longitude, height and time parameters of an aviation flight path, and calculates a standard atmospheric model and a cosmic ray energy spectrum in combination with the standard atmospheric model and the cosmic ray energy spectrum. The data processing module is used for calculating theoretical dose rates and particle type distribution of different height levels based on a Monte Carlo method and outputting the theoretical dose rates and the particle type distribution, and the data correction module is used for dynamically correcting parameters and weights of simulation results by combining measured data based on a deep learning algorithm of deep learning, so that adaptive fusion of the simulation data and the measured data is realized; and the ground radiation detection module comprises a semiconductor radiation detector, a data acquisition and preprocessing unit, a remote control module and a network communication module. The system has the characteristics of high real-time performance and wide coverage range.

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 relates to the technical field of electroplating wastewater treatment, and discloses an electroplating wastewater pollution early warning and regulation system based on Internet of Things, comprising: a sensing layer for collecting characteristic parameters of an electroplating wastewater treatment link through distributed sensing nodes, the characteristic parameters including a water quality parameter, an equipment operation parameter and an environmental parameter, a wireless communication module is arranged in the sensing node; the network layer is used for constructing a wireless communication network and transmitting the parameters acquired by the sensing layer to the application layer through a multi-hop relay; and the application layer receives and processes the parameters transmitted by the network layer to realize pollution early warning, pollution risk prediction and regulation and control instruction generation, and the regulation and control instruction is fed back to the execution layer through the network layer. By adopting a channel frequency hopping mechanism, when it is monitored that the interference intensity of a current communication channel exceeds a preset interference threshold value, the standby channel can be automatically switched, instruction transmission of an application layer and an execution layer is not interrupted, and it is guaranteed that data such as water quality parameters and equipment operation parameters collected by a sensing layer are continuously and stably transmitted to the application layer.

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 an electric vehicle freight planning system based on a charging and battery replacement hybrid strategy. The electric vehicle freight planning system comprises a network construction module, an optimization model module and an algorithm solving module. The network construction module conducts modeling through a space-time convolution network, time is dispersed into a plurality of time points, space is defined as freight station nodes, and a charging arc, a service arc and a convolution arc are constructed. And the optimization model module establishes a mixed integer programming model based on the network, wherein a target function minimizes the total transportation cost, and constraint conditions comprise commodity flow balance, transport capacity limitation, purchase budget and power conversion capacity constraint. The algorithm solving module adopts a column generation algorithm to generate a path ring, and solves a sub-problem in combination with multi-dimensional dynamic programming; the dynamic planning transmits and updates the electric quantity state through hierarchical labels, and applies a dominating rule to screen a non-inferior solution path. The system intelligently triggers battery replacement or charging according to the electric quantity state, optimizes an electric truck energy complementing strategy and a freight path, improves the transportation efficiency, reduces the energy consumption cost, reduces the unloaded driving time, and improves the utilization rate of a motorcade.

The invention relates to the technical field of new energy automobile charging, and discloses a charging pile gun address dynamic allocation method and system based on power topology identification. A host control module, a terminal equipment module, a charging gun access module, a power transmission detection module, a network communication interaction module, an address intelligent distribution module, a fault diagnosis isolation module and a data persistent storage module are established; the host control module is connected with the charging gun access module, the power transmission detection module and other subsequent function modules through power lines, each module is correspondingly connected with a terminal and a charging gun, a system physical power topology and a multi-terminal network communication topology are constructed based on a hardware connection relationship, and the host and the terminal finish power-on initialization respectively. And the host starts an address allocation preparation process, and the terminal starts network initialization and address request preparation, so that a plug-and-play function is realized, the deployment efficiency and operation and maintenance reliability of the charging pile are improved, and finally the problems of low efficiency, easy error and complex after-sales maintenance of traditional manual address configuration are solved.

The invention relates to the technical field of kitchen appliance control, and discloses a kitchen appliance integrated control system based on dynamic power distribution, which comprises a power monitoring module, a load analysis module, a dynamic distribution module, a safety protection module, a user interaction module and a network integration module. The current, voltage and instantaneous power consumption value of each device are collected in real time through a power monitoring module, and whether a safe power interval is exceeded is judged; analyzing a total load state and a dynamic demand trend based on a load analysis module, and predicting a potential overload risk; the dynamic allocation module dynamically adjusts the power allocation proportion of key and non-key equipment in combination with a user priority rule; the safety protection module monitors in real time and automatically executes graded protection measures; and the user interaction module provides a graphical interface to support priority configuration and power upper limit setting so as to realize strategy closed-loop optimization.

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 discloses a chicken feed production line intelligent ingredient regulation and control system based on Internet of Things edge computing, which relates to the technical field of feed production management and comprises an intelligent sensing terminal module, an edge computing center module, a cloud management and control platform module, a network communication module, an intelligent algorithm control module and an execution control module. The intelligent sensing terminal module is used for collecting ingredient production data of a chicken feed production line in real time through a sensor. Through a double-strategy cooperation mechanism of the intelligent algorithm control module, raw material conveying lag is compensated in advance through model prediction control, disturbance of humidity, vibration and the like is corrected in real time through self-adaptive fuzzy control, batching errors are effectively reduced, uneven nutrition of chicken flocks caused by formula errors is avoided, and a high-precision execution mechanism in the execution control module is matched, so that the control accuracy is improved. Through frequency conversion control dynamic matching feeding rotation speed and near infrared spectrum uniformity monitoring, the nutrition consistency is improved, and the breeding growth income is remarkably improved.

The invention discloses a port energy and operation system cooperative load prediction method, and relates to the technical field of energy management and load prediction, and the method comprises the steps: obtaining historical port data organized according to a time sequence; training a load prediction model according to historical port data to obtain a trained load prediction model; the load prediction model comprises a feature gating mechanism module, an LSTM network module, an attention fusion module and a prediction module; and acquiring real-time port data, and performing load prediction by using the trained load prediction model. The method can adapt to the complex characteristics of a port multi-energy system, and improves the prediction precision and robustness of the port power load.

The invention provides intelligent tour guide system equipment based on an artificial intelligence (AI) intelligent agent and a charge pal function, and belongs to the technical field of text travel service and intelligent terminal fusion. According to the system, core components such as a large language model (LLM), a scenic spot knowledge base, a positioning module, a voice interaction module, a power module and a network module are integrated, so that the functions of personalized route planning, multi-modal interaction explanation, automatic guide triggering, real-time voice question and answer and the like are realized, and power supply or charging for other electronic equipment is supported. The system has a context understanding capability, can dynamically optimize tour guide contents, and improves the tour experience of tourists. The problems that traditional tour guide equipment is poor in interactivity and lack of individuation and cannot meet diversified requirements of users are solved, and the tour guide equipment has good industrial application prospects.

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 application relates to a complex network disease propagation tracing method based on a graph neural network. The method comprises the following steps: firstly, an individual infection sample on a contact network is obtained by using a propagation dynamics model; then, node topological features on the contact network are obtained based on a centrality method, and the individual infection sample is added; then, data preprocessing is performed on the individual infection sample, and the individual infection sample is divided into a training sample, a verification sample and a test sample; then, the training sample and the verification sample are put into a neural network model based on a message passing mechanism for training, wherein the neural network model based on the message passing mechanism comprises a feature encoding module, an MPNN network, a propagation controller module and a priori knowledge module; finally, the test sample is input into the trained neural network model based on the message passing mechanism, and a prediction result of a propagation source of the test sample is obtained. The propagation source on the complex network can be efficiently and accurately inferred.

This invention relates to cloud PC servers, including a 3U standard rack-mount server. The server comprises compute blades housed in a chassis, a fan module, a power supply module, a network switching module, and a main control management module responsible for monitoring and managing the operational status of the system hardware. The compute blades are configured with 24 blades, each capable of independent operation. The fan module employs a front-to-back airflow cooling method. Two power supply modules are provided, allowing for current sharing and hot backup. The network switching module features two independent and physically isolated networks. This invention exhibits good scalability and versatility, and high reliability.

The utility model relates to a distributed double-control server and a distributed double-control system. The distributed double-control server comprises a distributed computing module, a network switching module, a first processing module and a second processing module, the first processing module and the second processing module are connected with the distributed computing module through a management bus; the first processing module and the second processing module are connected with the distributed computing module through the network switching module; the first processing module and the second processing module are connected through a keep-alive bus; when any one of the first processing module and the second processing module works, the other processing module does not work; the processing module during working is used for controlling the distributed computing module to execute a computing task; the problems of low computing efficiency and poor reliability in a traditional scheme are solved, the distributed computing module is interconnected with the dual-processing module through the network switching module, and the performance of the computing server is improved.

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 dynamic energy-saving control system for a repeater, and belongs to the technical field of communication. The system comprises an AI computing server for deploying an AI learning and prediction model, a network management server responsible for communication transfer and equipment management, a multi-mode sensing module comprising a millimeter wave radar, an infrared pyroelectric sensor and an environment sensor array, a network access module and a power regulator. The repeater transmits the acquired data to a network management server through a network access module, and then transfers the acquired data to an AI computing server; the AI calculation server calculates the target occurrence probability P (t) through a multi-source data fusion algorithm, controls the power regulator to achieve three-level power regulation, solves the problems that a traditional repeater is low in energy saving efficiency and unbalanced in space-time utilization rate, and improves the energy utilization rate and the communication availability.

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.