9 results about "Network tomography" patented technology

The application discloses a diagnosis monitoring method and system based on a tree topology, and the method comprises the following steps: a controller generates an I / O module diagnosis information acquisition task and an I / O network diagnosis information structure according to a configuration file; the diagnosis information acquisition task acquires diagnosis information periodically, and writes the diagnosis information into the I / O network diagnosis information structure; the controller combines a tree I / O network topology relationship to judge the states of network elements in a step-by-step manner from bottom to top according to the I / O module diagnosis information and the I / O network diagnosis information; and configuration software acquires the states of I / O network topology elements after judgment and collection from the controller, and displays the states in a graphical manner. The technical scheme can realize intuitive display of abnormal elements of each level of the I / O network, realize abnormal collection from bottom to top, and improve the online monitoring experience.

The application discloses an SSL VPN tunnel transmission method for an IP layer, and relates to the technical field of network communication.The method constructs an IP virtual tunnel outside an SSL session, and directly encapsulates and transmits complete IP messages, thereby completely breaking through the limitation that a traditional SSL VPN only supports a Web proxy, and transparently passing through the tunnel for TCP services, UDP streaming media, ICMP network diagnostic messages, and the like, so that the method realizes remote access of the IP layer in a true sense, and a server adopts a south-north decoupling design, a virtual network port is used as a southward interface to communicate with a client tunnel, and the northward directly interacts with an intranet physical network card through a system routing table, without analyzing transport layer content, without maintaining a TCP connection state, and only performing IP layer transparent forwarding, so that the processing cost is greatly reduced, and the throughput performance is obviously improved.

The application provides a field bus remote diagnosis method and related device, and belongs to the technical field of industrial communication network diagnosis.The application solves the problems that the traditional field bus diagnosis system is difficult to realize real-time monitoring of remote equipment and remote communication is susceptible to electromagnetic interference, resulting in low data transmission reliability.The device comprises an optical communication unit, a data processing unit and a diagnosis analysis unit;the optical communication unit performs photoelectric conversion on the received optical signal and outputs a clock synchronization electrical signal;the data processing unit decodes and protocol analyzes the field bus sending data;the diagnosis analysis unit extracts the slave station response delay time, frame error rate, signal amplitude attenuation coefficient and bus load rate based on the analyzed data, and identifies faults accordingly.The application adopts optical communication transmission and adaptive clock recovery technology, realizes high-reliability remote real-time monitoring and intelligent fault diagnosis against strong electromagnetic interference within a range of 10 kilometers, and can be widely applied to large industrial fields such as thermal power generation and petrochemical industry.

This application provides a vehicle fault diagnosis method and related equipment, belonging to the field of vehicle intelligent diagnosis technology. The method includes: collecting multi-source heterogeneous data from the target vehicle across the entire domain; preprocessing the collected preset multi-source vehicle data to obtain vehicle feature vector data; mapping vehicle risk based on the vehicle feature vector data using a preset multi-dimensional evaluation unit to obtain vehicle risk severity data; predicting fault state classification based on the vehicle risk severity data and the vehicle feature vector data using a preset random weighted neural network diagnostic model to obtain vehicle operating state energy level data; and performing causal logic encoding based on the vehicle operating state energy level data using a preset large language model to obtain abnormal state causal tracing results. This application embodiment can realize real-time fault diagnosis and root cause tracing of vehicles under multi-physics coupling.

The application discloses a network anomaly diagnosis system, an operation and maintenance management device and a network diagnosis method thereof. The network diagnosis method uses a network diagnosis model to detect log data. The network diagnosis model is trained by using a log data set provided by a GAN neural network model trained by a method for training the GAN neural network model. The method for training the GAN neural network model comprises the following steps: acquiring data; performing data enhancement on the data by using the GAN neural network model, and outputting virtual data; selecting important data with important features from the virtual data, and performing data synthesis on the important data by using a loss function, so as to realize sample enhancement of the important data.

This invention discloses a cloud network scenario-based diagnostic platform based on decision trees for end-to-end full-link network diagnosis. Addressing the root cause diagnosis problem when tenant end-to-end traffic anomalies occur, this invention proposes a multi-node full-link network diagnostic scheme based on decision trees. It designs decision logic based on expert experience, solving the decoupling and judgment of complex decision logic through root cause judgment with multiple combinations of conditions. It transforms the original independent condition judgments into state machine-based judgments, overcoming the challenges of long access links, numerous anomaly information, and difficulty in comprehensive root cause judgment faced by traditional diagnostic technologies. This diagnostic platform can be deployed on large-scale production cloud network operation and maintenance platforms and can be widely applied to the anomaly diagnosis of various network components such as virtual private networks, public network access, load balancing, and leased line access.

The application discloses a network diagnosis method, a network diagnosis device and a computer storage medium. The network diagnosis method comprises the following steps: in response to a bandwidth-limited signal, acquiring an output code rate of a current network and a network bandwidth parameter in a preset time interval, wherein the network bandwidth parameter comprises a transmissible data amount and a spectrum efficiency of the current network; determining whether the transmissible data amount is lower than the output code rate; if yes, determining whether the spectrum efficiency is lower than a historical spectrum efficiency; if yes, and a difference between the spectrum efficiency and the historical spectrum efficiency is greater than a first preset difference threshold, determining that a cause of the current network bandwidth limitation is channel attenuation. In the foregoing manner, the application can quickly determine the cause of the network bandwidth limitation based on the size of the transmissible data amount and the spectrum efficiency in the network bandwidth parameter, thereby improving the diagnosis efficiency of the network bandwidth.

This invention provides a transformer fault diagnosis method and system based on multi-physics coupling and digital twin embedding, belonging to the field of power equipment condition monitoring technology. The method includes: collecting and preprocessing multi-source data on transformer vibration, leakage flux, temperature, and oil chromatography; constructing a digital twin of the transformer; deriving a health baseline value; comparing the measured values ​​with the health baseline value; calculating the multi-physics residual sequence; extracting features from each physical field residual sequence; constructing a multi-physics coupling feature map; adaptively fusing features from the coupling feature map based on an attention mechanism; diagnosing the fault type and location through a physical information neural network; applying physical consistency loss constraints; and finally performing closed-loop optimization and model updating, using new samples to achieve digital twin correction. This invention achieves deep multi-physics fusion, embedding physical mechanisms into a data-driven model, improving the accuracy, interpretability, and early warning capabilities of fault diagnosis.

This invention discloses an intelligent diagnostic method and system for rolling bearing faults based on neural networks. The diagnostic method includes: sampling the vibration signal of the rolling bearing and outputting the sampled signal; performing resonance demodulation processing on the sampled signal using a pre-constructed adaptive Mel filter bank to extract a frame-level feature vector sequence; and using a pre-trained neural network diagnostic model to perform multi-dimensional feature extraction, fusion, and classification operations on the frame-level feature vector sequence to obtain the fault type of the rolling bearing. This invention enhances fault characteristics by performing resonance demodulation processing on the sampled signal using a pre-constructed adaptive Mel filter bank; and achieves feature extraction and classification using a neural network diagnostic model, effectively suppressing background noise interference, improving diagnostic accuracy, and supporting the identification of various bearing faults.