43 results about "Network tomography" patented technology

The invention discloses a vehicle fault diagnosis method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly obtaining the voice description information of a target user for a target vehicle, and then determining the vehicle feature information and fault feature information corresponding to the target vehicle based on the voice description information, determining k electronic control units needing fault diagnosis in the target vehicle based on the fault feature information, and determining n network diagnosis protocols based on the vehicle feature information and the k electronic control units, obtaining first fault data corresponding to the k electronic control units based on the n network diagnosis protocols, and finally performing fault diagnosis on the k electronic control units based on the first fault data to obtain a first fault diagnosis result. According to the embodiment of the invention, the efficiency of vehicle fault diagnosis is improved.

The invention discloses a fan variable pitch bearing intelligent monitoring method and system based on multi-parameter fusion, and the method comprises the steps: respectively calculating angles based on a three-axis acceleration and a three-axis angular velocity, and obtaining a final angle through complementary filtering fusion; carrying out ensemble empirical mode decomposition on the three-axis vibration signals, respectively extracting low-frequency vibration characteristics, high-frequency vibration characteristics and angle variation based on a plurality of signal components, carrying out fusion decision on the low-frequency vibration characteristics, the high-frequency vibration characteristics and the angle variation by adopting a D-S evidence theory to obtain a total confidence coefficient, and judging a fault in combination with a preset fault threshold to obtain a first fault diagnosis result; the low-frequency vibration characteristics, the high-frequency vibration characteristics and the angle variation are input into a deep residual network diagnosis model for processing, and a second fault diagnosis result is obtained; and comparing the first fault diagnosis result with the second fault diagnosis result, outputting a final diagnosis result if the first fault diagnosis result is consistent with the second fault diagnosis result, and performing diagnosis again if the second fault diagnosis result is inconsistent. The limitation of a single sensor frequency band is broken through, a D-S evidence theory is innovatively adopted to carry out multi-source evidence fusion decision, and the fault recognition rate is improved.

The invention discloses a nuclear power plant graph attention network diagnosis method based on fault causal guidance, and relates to the technical field of nuclear power plant fault diagnosis. In order to solve the technical defect that a nuclear power plant fault diagnosis method in the prior art does not fully utilize causal information in a knowledge-driven method, the technical scheme provided by the invention comprises the following steps: acquiring operation data and high-precision simulation data of a preset number of subsystems of a nuclear power plant; carrying out fault state labeling on the simulation data; establishing a causal propagation path between the sensor nodes; extracting time sequence features of the sensor data, and constructing a time-space fused fault propagation graph; hierarchical aggregation of fault features is realized; feature extraction is carried out on the sensor data, and local modes and key fault features in the data are captured; optimizing the connection weight of the same-order neighbor nodes in the fault propagation graph; and calculating hidden variable representation, and outputting a fault diagnosis result. The method is suitable for nuclear power plant operation monitoring, intelligent fault diagnosis, safety early warning and the like.

The invention relates to a network diagnosis method and an industrial Ethernet master-slave system based on an FPGA. The method is applied to an FPGA unit of a master station in the industrial Ethernet master-slave system based on the FPGA, the system further comprises a plurality of slave stations connected with the FPGA unit, and the method comprises the steps of obtaining communication load data of the industrial Ethernet master-slave system based on the FPGA, judging whether the industrial Ethernet master-slave system based on the FPGA has idle bandwidth or not based on the communication load data, and if yes, sending the idle bandwidth to the FPGA unit. Under the condition that the industrial Ethernet master-slave system based on the FPGA has idle bandwidth, a diagnosis message is inserted into a data frame gap of the industrial Ethernet master-slave system based on the FPGA, network diagnosis operation is sequentially executed on a plurality of slave stations through the diagnosis message, diagnosis feedback data of the plurality of slave stations and diagnosis feedback data based on the plurality of slave stations are received, and the diagnosis feedback data of the plurality of slave stations are sent to the plurality of slave stations. And determining a network diagnosis result. By adopting the method, the network diagnosis efficiency can be improved.

The utility model relates to the technical field of network equipment, in particular to a network equipment interface with a network diagnosis function, which comprises an equipment body, interface slots are arranged at two ends of one side of the equipment body, connector lugs are arranged on the interface slots, clamping slots are arranged at two sides of the connector lugs, and diagnosis modules are arranged in the interface slots. The diagnosis module comprises a data capture unit, a connection state monitoring unit and a fault alarm, whether the wiring is normally connected or not can be detected, if looseness or falling is found, an alarm is triggered immediately, and the fault alarm unit can give an alarm through an LED lamp and a buzzer, remind a user to take measures and record related event logs, so that subsequent analysis is facilitated; and the connector lug is matched with the clamping block on the moving plate through the clamping groove, so that the connector lug can be firmly fixed on the interface groove, the mechanical stability of the interface is remarkably improved, the structure is simple, the implementation is easy, and the influence of network interruption on a user is reduced through a fault alarm function.

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 invention provides a network diagnosis method and device based on a software defined network (SDN), and electronic equipment. The method comprises the following steps: obtaining to-be-detected service data, wherein the to-be-detected service data comprises one or more of the following items: service configuration data, service issuing data and service receiving data; network connectivity diagnosis is carried out on the SDN based on the service data through a flow diagnosis model, a diagnosis result is obtained, and the diagnosis result is used for indicating whether the service of the SDN is normally configured or not and whether the service of the SDN is normally issued or not; performing data reconciliation on the SDN based on the service data through a service reconciliation model to obtain a reconciliation result, the reconciliation result being used for indicating whether the service of the SDN is normally received; therefore, comprehensive detection of service configuration, issuing and receiving processes in the SDN network is realized.

According to embodiments of the present disclosure, a method, an apparatus, a device and a storage medium for training a network diagnosis model are provided. The method comprises: simulating a set of network states by using a network simulator; constructing a plurality of training samples respectively corresponding to the plurality of network states based on the simulation of the set of network states, wherein each training sample in the plurality of training samples comprises at least device information related to a network state corresponding to the training sample, and the plurality of network states are obtained based on the set of network states; for each training sample in the plurality of training samples, generating a model diagnosis result for the network state corresponding to the training sample by using the network diagnosis model based on the device information related to the network state, wherein the network diagnosis model is configured to perform diagnosis on the network state; and updating the network diagnosis model based on the model diagnosis results respectively generated for the plurality of training samples and diagnosis reference information respectively corresponding to the plurality of training samples.

The invention discloses a cloud platform network connectivity detection method and a related device, the method is applied to a client, and the method comprises the following steps: newly establishing a test resource object and sending a new establishment instruction to each server in a cluster, so that each server also newly establishes the test resource object after receiving the new establishment instruction; after a test resource object in the client is controlled to enter a network namespace of a target resource object in the client, at least sending a network diagnosis instruction to other target resource objects in the client and the target resource object in the server by using the identity of the target resource object; sending a first instruction to a server; and obtaining a network test result fed back by each test resource object in the client and the server. The method can ensure that the target resource object is not influenced in the inspection process.

The invention discloses an infrared thermal imaging safety interlocking map neural network diagnosis system, relates to the technical field of infrared thermal imaging detection, and is used for solving the problem that a traditional system is difficult to warn early-stage composite faults caused by heat conduction between devices. The data processing module constructs a graph structure fusing the thermodynamic data and the equipment topological relation; the graph neural network diagnosis module performs deep learning based on the graph structure and dynamically identifies a fault mode; and the safety interlocking execution module intelligently triggers graded protection according to the diagnosis result. According to the invention, integrated closed-loop safety control from state perception and intelligent diagnosis to active protection is realized, and the diagnosis precision and response speed of the industrial equipment safety protection system are effectively improved.

The invention belongs to the technical field of data communication, and provides a real-time network communication system and method, a storage medium and equipment. The real-time network communication system comprises a data grouping module, a dynamic priority scheduling module, a composite network diagnosis module and a data security transmission module, the data grouping module is used for grouping the acquired communication data; the dynamic priority scheduling module is used for performing dynamic priority scheduling on the communication data through a dual-priority queue scheduler and an event driving mechanism based on a data grouping result; the composite network diagnosis module is used for carrying out composite network diagnosis by deploying an embedded broadcast probe and calculating the bus health degree to carry out health degree evaluation; and the data security transmission module is used for performing encryption processing on the data of different groups based on the data grouping result and verifying data access and network connection. According to the real-time network communication system, the communication performance, the real-time performance, the reliability and the safety are improved.

The invention discloses a fault diagnosis method and system for a crane hook lifting motor, and belongs to the field of motor monitoring. The method comprises the following steps: acquiring multi-modal operation data of a crane hook lifting motor in real time through a multi-sensor synchronous acquisition system, constructing a physical information neural network (PINN) diagnosis model, respectively performing fast Fourier transform (FFT) on a vibration signal, a current signal and an audio signal, extracting spectrum characteristics of each signal, and the like. According to the method, physical laws such as an electromagnetic induction law and a heat conduction equation are embedded into a neural network loss function, so that a diagnosis result has a clear physical explanation, an engineer is helped to quickly position root causes (such as winding short circuit and bearing pitting) of faults, and scientificity and pertinence of maintenance decisions are remarkably improved. The innovative frequency domain fusion mechanism dynamically distributes the weights of vibration, current and audio features based on mutual information entropy, effectively strengthens the cooperative expression of fault sensitive information, and overcomes the limitation of traditional single-mode diagnosis.

The application discloses a kind of multi-source data's reliable alignment method, device, equipment, storage medium and product.Identify the discontinuous fragment of the measurement data sequence of each sensing device and associate sensing device identification, obtain measurement data variation section set;Determine the data channel of each associated sensing device based on sensing device identification and determine overlapping path section through network diagnosis, generate path network atlas;The target measurement data sequence that flows through each overlapping path section is segmented and processed to obtain target data segment, and different target data segments that flow through each overlapping path section at the same time are subjected to data flow consistency analysis to determine an interference channel list;Each target data segment in the target measurement data sequence via the interference channel is subjected to validity determination, and the effective data segment and the measurement data sequence of the non-interference channel are aligned and combined according to time to obtain multi-source reliable alignment data.It ensures data quality, avoids distortion information mixed into fusion results, and improves the accuracy and reliability of downstream decision-making.

The invention discloses an electric power WAPI network fault diagnosis method. The method comprises the following steps: periodically collecting monitoring index data for network fault diagnosis; reporting the collected monitoring index data to a WAPI network diagnosis system; the WAPI network diagnosis system carries out network fault diagnosis based on a network fault diagnosis model qualified in training, the network fault diagnosis model comprises an LSTM sensing network and a multi-classification network, the LSTM sensing network is used for receiving reported monitoring index data, establishing sensing learning ability for network characteristics changing along with time and outputting intermediate variables to the multi-classification network, and the multi-classification network is used for carrying out network fault diagnosis on the basis of the intermediate variables. And the multi-classification network is used for taking an intermediate variable output by the LSTM sensing network as an input, realizing fault type identification based on a multi-layer neural network, and outputting the identified fault type. According to the invention, automatic diagnosis of the power WAPI network fault is realized, the average fault positioning time is effectively reduced, and the operation and maintenance cost is reduced.

The invention relates to the technical field of communication, in particular to a network diagnosis method and device for an SIM card, equipment and a storage medium. Comprising the following steps: in response to a diagnosis instruction for the SIM card, obtaining a life state and a public network on-off state of the SIM card; the diagnosis instruction comprises an SIM card identifier of the SIM card, the life state is used for representing availability and a service attribute state of the SIM card, and the public network switch state is used for representing whether the SIM card is allowed to access a public network data service; when it is determined that the diagnosis result cannot be called from the cache of the SIM card according to the life state, the public network on-off state and the SIM card identifier, the real name state and the flow state of the SIM card are obtained; the traffic state is used for representing the traffic use condition of the SIM card; and determining a diagnosis result of the SIM card according to the real name state, the flow state, the life state and the public network on-off state of the SIM card. According to the embodiment of the invention, manual intervention and experience dependence can be reduced, the diagnosis process is simplified, and the diagnosis efficiency and accuracy are improved.

Methods, devices and computer program products for network diagnosis are described. In an example method, a user query on the network diagnosis expressed in a natural language is obtained. A diagnosis result is obtained. The diagnosis result is resulted from execution of a target workflow including a set of machine-executable tasks for a network diagnosis process on a target category of the network diagnosis, wherein the target workflow is determined based on the user query and the target category is associated with one or more network components. The diagnosis result is output to a user from which the user query is received.

The present application relates to the technical field of data processing, and discloses an intelligent checking method and system for esophageal cancer data entry link, comprising: obtaining an esophageal cancer medical report from a database; preprocessing the esophageal cancer medical report to obtain training data; making type labels of the training data set; respectively constructing a medical image data feature extraction network, a diagnosis text data feature extraction network and a feature vector dynamic splicing network, and training the medical image data feature extraction network, the diagnosis text data feature extraction network and the feature vector dynamic weighted splicing network, and saving the trained network; using the network to extract medical image data features and diagnosis text data features, dynamically weighting and splicing the two kinds of data features and inputting them into a softmax to output a medical record category probability, and after calculating a divergence value, judging whether the entry type is correct; this method can greatly improve the work efficiency of the operator and improve the accuracy and reliability of the entered data.

A method is proposed for network diagnosis. In the method, a user query on the network diagnosis expressed in a natural language is obtained. A target workflow including a set of machine-executable tasks for a network diagnosis process on a target category of the network diagnosis is determined based on the user query and a diagnosis result is generated by performing the set of machine-executable tasks in a network diagnosis process.

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.

The invention discloses an asynchronous motor time-space diagram network diagnosis method and system fused with a physical model, belongs to the technical field of industrial automation, and solves the problems that an existing asynchronous motor fault diagnosis method neglects a sensor spatial topological relation, lacks physical mechanism guidance and is poor in generalization ability under variable working conditions. Comprising the steps that multi-source heterogeneous data of an asynchronous motor are collected and preprocessed, and a signal snapshot sequence of a multi-sensor physical topological graph is obtained; using the preprocessed data to construct and train a PST-GATN model; and performing forward propagation on multi-source heterogeneous data of the asynchronous motor by adopting the trained PST-GATN model to obtain a diagnosis result. The method is suitable for asynchronous motor fault diagnosis scenes.

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 invention discloses a terminal network anomaly diagnosis and troubleshooting method and a readable storage medium, and the method comprises the steps: obtaining operation information, generating a corresponding operation log, and generating a first index record according to the operation log; performing hierarchical network diagnosis on the terminal and positioning the hierarchy of the network anomaly based on triggering of the network anomaly of the terminal, and generating a corresponding diagnosis log and a second index record; recording the trigger time of the hierarchical network diagnosis, adding an association identifier to the operation log in a preset duration range before and after the trigger time, and marking the operation log as a fault window log; and when detecting that the network is recovered, uploading the diagnosis log and the fault window log to the cloud based on the first index record and the second index record. According to the method, the key running track can be completely reserved when the network exception occurs, the most valuable diagnosis basis is uploaded at the first time when the network is recovered, rapid positioning, accurate redisk and remote troubleshooting of the network exception are realized, and the fault processing efficiency is greatly improved.

A link-level network tomography and attack detection method based on a conditional random field relates to the field of communication, and comprises the following steps: acquiring a routing matrix and a link prior congestion probability, acquiring a path state containing a time sequence rule from samples conforming to link-level first-order Markov property, and taking the path state as a time sequence sample pool; performing feature modeling by combining the routing matrix, the link prior congestion probability and the path state to generate a link-level feature vector; the link-level feature vectors and the corresponding actual link state tags are transmitted into a CRF model for training, and state transition weight parameters and state feature corresponding weight parameters are obtained through training; performing network state prediction by using the trained CRF model; and carrying out attack judgment on the predicted network state. According to the method, the space-time relevance between state labels is obviously modeled, and the data relevance between time steps can be better processed by fusing topology perception characteristics and time sequence state transition modeling, so that the high-precision detection of link-level anomalies is realized, and the calculation complexity is reduced.

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.

The present application relates to the technical field of geophysical exploration, and discloses a local fine velocity modeling method, device and electronic equipment, the method comprising: establishing an initial velocity model according to velocity information of well data, velocity information of first arrival inversion and prestack time migration velocity information; optimizing and iterating the initial velocity model through a network tomography inversion method to obtain an optimized initial velocity model; performing local ray tracing on a structure imaging unreasonable area to determine a ray path corresponding to the structure imaging unreasonable area; and performing local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow layer velocity model. The present application can obtain a more accurate shallow layer velocity model, thereby improving the imaging quality of data in a double complex area.

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 invention provides a connection optimization method and system for a local area network server and related equipment, and the method comprises the steps: the local area network server reports an intranet IP to a cloud public server, and a client obtains the intranet IP and tries to establish local area network direct connection with the local area network server; the cloud public server periodically counts the connection modes of the clients, and when it is found that the connection modes of all the clients connected to the local area network server in the statistical period are forwarding through the cloud gateway, it is judged that intranet IP configuration is abnormal and an alarm is generated; and when the client needs network diagnosis of the local area network server side, the client sends a network detection instruction to the local area network server through the WebSocket long connection, and the local area network server feeds back a result to the client after executing the network detection instruction, so that remote diagnosis of the quality of a link from the local area network server to the cloud gateway is realized. According to the invention, by constructing a mechanism for periodically carrying out abnormity monitoring and cross-end diagnosis on the client connection mode, the reliability of local area network connection and the troubleshooting efficiency are improved.

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.

The invention relates to a network dynamic optimization method and device, computer equipment and a medium, and the method comprises the steps: continuously monitoring the connection state data of equipment nodes in a smart home network, so as to detect an equipment node change event; in response to the equipment node change event, starting a network diagnosis process based on current network state data of the smart home network, the network state data including network performance index data and network configuration and topology state data of each equipment node; determining a network anomaly type based on the network state data in the network diagnosis process, and generating a network self-healing strategy corresponding to the network anomaly type; and executing the network self-healing strategy to optimize the network performance of the smart home network so as to end the network diagnosis process. According to the invention, automatic diagnosis and self-healing of the smart home network fault can be realized.