24 results about "Network error" patented technology

The invention provides a rice irrigation online learning forecasting method and system, the method is realized according to a pre-constructed physical mechanism-neural network hybrid model based on physical mechanism model prediction and neural network error correction fusion, and the method comprises the following steps: S1, obtaining real-time environment data of a current decision period of a target rice field; s2, on the basis of the real-time environment data of the current decision period, forecasting a paddy field water layer depth predicted value of the next decision period through a physical mechanism-neural network hybrid model, and generating an irrigation drainage forecast in combination with a crop irrigation drainage mode; s3, real-time environment data of the target rice field in the next decision period after irrigation drainage forecast is executed are obtained, training samples are constructed and put into an experience playback pool, and the physical mechanism-neural network hybrid model executes online learning based on the experience playback pool; and S4, when the next decision cycle starts, returning to S2 until a preset stop condition is met.

The invention discloses a composite game reinforcement learning method and device for a four-rotor unmanned aerial vehicle cluster, and relates to the technical field of unmanned aerial vehicle cluster control. The method comprises the steps that an evaluation-execution network of the unmanned aerial vehicle is used for directly approaching a value evaluation and cooperation strategy online in a state-control input data space of cluster flight, repeated offline solving of a high-dimensional coupling HJ equation set under strong coupling nonlinear dynamics of the unmanned aerial vehicle is avoided, and therefore the modeling precision requirement and the calculation overhead are remarkably reduced. By introducing a filtering mechanism with a'forgetting factor ', weighted integral processing is performed on samples such as pose / speed / relative formation errors, control instructions, energy consumption and safety cost collected in the historical flight process of a cluster, and a composite'evaluation' network error and regression signal fusing current and historical information is constructed; and a finite excitation criterion which can be inspected on line is given on an information matrix level, so that the unmanned aerial vehicle cluster can still keep effective learning under the condition that a continuous PE condition is not met.

This invention discloses a method and system for intelligent diagnosis and optimization of network anomalies in iOS device apps. The method specifically includes: parsing system logs to identify network error patterns, and temporally associating the identified network error patterns with user operation event sequences to form a preliminary diagnostic event chain; extracting features and performing semantic understanding on interface screenshots and voice descriptions, and matching them with a preset fault knowledge base to obtain preliminary fault inferences; constructing a relationship graph based on the preliminary diagnostic event chain and preliminary fault inferences, calculating the probability of each node as the root cause of the network anomaly, and outputting the root cause location result; and generating a targeted personalized configuration optimization suggestion sequence based on the root cause location result through interactive exploration. This invention achieves accurate diagnosis and personalized optimization of network anomalies in iOS device apps, enabling rapid location of the root cause of the problem, reducing the time and cost of manual troubleshooting, and improving fault handling efficiency and user experience.

A device, communication system, and method are provided. In one example, a system for reducing latency in out-of-order data transfers is described that includes circuits to detect a network error; and in response to detecting the network error, transmit a notification to at least one of a plurality of connected nodes that the network error was detected, wherein the notification causes each node that receives the notification to change at least one transport layer parameter.

The application relates to a control method for dynamic walking of a biped robot and the biped robot, and belongs to the technical field of robot control, which comprises the following steps: 1, a self-recurrent cerebellar model neural network is used to establish a dynamic model of the biped robot with a disturbance term, and dynamic robust walking of the biped robot is converted into a problem of realizing stability of a multi-input multi-output nonlinear system with a bounded uncertain term; 2, an adaptive self-recurrent cerebellar model neural network error observer is designed to estimate an error upper limit; 3, an adaptive law of network weight is designed to realize real-time updating of the network weight space and to adjust parameters of each learning factor; and 4, a boundary value estimation algorithm is used to compensate for an estimation error and feedback to a robot walking system, so that the biped robot can realize asymptotic stable walking. The application enables the control system to adapt to time-varying characteristics of the biped walking system on line, and has continuous learning and adaptive capacity for unknown dynamics.

The present application relates to the technical field of network error monitoring, and is a kind of network NO_SOCKET error monitoring and analysis method, comprising the following steps: user state program initializes two eBPF programs and storage structure;Two eBPF programs are respectively mounted to socket release function and skb release function;First eBPF program reports sock event entry to user state program;User state program stores it to storage structure;User state program carries out aging treatment to sock event entry;When kernel calls skb release function and second eBPF program judges that the reason is NO_SOCKET error, second eBPF program reports skb event entry to user state program;User state program finds sock event entry from storage structure based on skb event entry, and obtains error processing information, to realize the monitoring and analysis of NO_SOCKET error.

The invention provides a cogeneration system active environment coordination control method based on distributed reinforcement learning, and belongs to the technical field of power system operation and control, and the method specifically comprises the following steps: S1, building an active scheduling model of a cogeneration system; s2, determining a solution strategy based on distributed reinforcement learning; and S3, iteratively updating the performance index and the neural network error. The distributed control strategy is adopted, the cogeneration units are regarded as independent agents, the neural network is used for estimating the performance index of each unit, the units are relatively independent, the sensitivity to the connectivity of the control signal paths is low, the problem that the optimization algorithm is interrupted due to the fact that one or more control signal paths are broken due to faults is solved, and the optimization efficiency is improved. And the robustness and safety of the active environment scheduling method are improved.

A device, communication system, and method are provided. In one example, a system for reducing latency in out-of-order data transfers is described that includes circuits to detect a network error; and in response to detecting the network error, transmit a notification to at least one of a plurality of connected nodes that the network error was detected, wherein the notification causes each node that receives the notification to change at least one transport layer parameter.

The application discloses a network error page display method and device, equipment, storage medium and product, and relates to the technical field of computers, which comprises the following steps: when an error occurs in accessing a website, obtaining a network error page corresponding to the error; generating explanation information corresponding to the network error page based on a large language model; modifying the content of the network error page according to the explanation information, obtaining a new network error page, and displaying the new network error page. Since the application generates explanation information corresponding to the network error page based on a large language model when an error occurs in accessing a website, modifies the network error page according to the explanation information, obtains a new network error page, and displays the new network error page, compared with the existing method of directly displaying the network error page when an error occurs in accessing a website, the above-mentioned method can display explanation information of the network error page that is easy for users to understand, helps users quickly analyze and locate network problems in the browser, and improves the user experience.

The application discloses a kind of equipment high-risk operation risk assessment method based on normal process, it is extended to the combination form of normal and normal process by traditional normal process, to better adapt to the evaluation needs of equipment high-risk operation;Thus the multiple sub-equipment involved in equipment high-risk operation is respectively solved by state monitoring data sub-equipment state failure time estimate value, and further solves risk assessment value;Finally, a kind of radial basis neural network with normal function and two normal functions as base function is established, and the network error is obtained by the existing equipment risk assessment historical data, and the adaptive weight adjustment law based on error feedback is further designed, and the neural network weight is trained until the error converges;Finally, the state monitoring value of the equipment to be evaluated is substituted, and the high-risk operation process risk assessment total score of the equipment to be evaluated is solved, and the risk assessment is completed.

The invention relates to the technical field of cross-platform application communication, in particular to a cross-platform sky-map component bidirectional communication method and device based on a message queue, and the method aims at the bidirectional communication requirements of an application layer and a WebView layer in a cross-platform sky-map component, manages concurrent requests through the message queue, generates a unique identifier for each instruction, and transmits the unique identifier to the application layer through the WebView layer. Accurate matching of messages is realized; data serialization and deserialization are combined to ensure that cross-layer data formats are uniform, main thread blocking is avoided through an asynchronous calling mechanism, and quick result feedback is achieved through timing polling; meanwhile, an error retry mechanism is designed for temporary faults such as network errors and WebView abnormity, and the communication reliability is guaranteed. According to the invention, a communication closed loop from the application layer to the communication layer to the WebView layer to the communication layer to the application layer is realized, the problems of concurrence processing disorder, easy blocking and weak fault recovery capability of the existing communication scheme are solved, the method is adaptive to a unit-app X framework and multiple mobile platforms, and the communication efficiency and stability of a cross-platform sky map component are improved.

The invention discloses a fusion processing system and method for multi-source heterogeneous data of an electric power system under a 5G network, and relates to the technical field of fusion processing of the multi-source heterogeneous data of the electric power system.The fusion processing method comprises the steps that phase angles, active power, voltage and time synchronization deviation of adjacent nodes are collected on a unified time axis, and paired phase angle difference observation and relative time deviation estimation are generated; an electrical expected phase angle difference is approximately obtained according to the small-angle power flow, and the relative time deviation is mapped into a 5G network projection phase angle according to the power frequency; subtracting the three values, normalizing according to a noise equivalent standard deviation to obtain an inconsistent quantity, finishing pairwise network removal correction and generating a weight matrix; and under the participation of the weight, performing weighted least square to obtain a state vector, and performing consistency back-check by using residual measurement, thereby realizing reliable fusion calculation under the condition of time delay fluctuation. According to the invention, network errors can be separated, and fusion precision and robustness are improved.

The application provides a train close tracking adaptive control method and device and a computing device. The method comprises the following steps: acquiring the position and reference speed curve of a front vehicle in a virtual marshalling train at a current time; combining the reference speed curve of the front vehicle at the current time to calculate a reference speed curve of the train; combining the position of the front vehicle at the current time and the position of the train to calculate an adjustment speed, and correcting a network error index by using the adjustment speed; training a radial basis function neural network by using the network error index to obtain a control force of the train; and controlling the train to run according to the control force. The method can realize adaptive control of the train, and further realize close tracking control of the marshalling train, and has the advantages of high real-time performance, high safety, high punctuality and high anti-interference performance.

The application provides a kind of neural network enhanced mechanical arm safety control method and system, the application constructs safety function for joint and workspace obstacle;Robust compensation term is designed by combining network weight error and inherent approximation error, and high-order safety constraint is converted into robust differential inequality;Single integrator auxiliary system and auxiliary barrier constraint are configured for each safety constraint channel, to maintain the feasibility of quadratic programming solution;According to stability driving item and safety driving, weight update law is constructed;And nominal joint torque, auxiliary reference input is used as optimization goal to construct quadratic programming problem, different working modes are switched by mode selection switch and solved, and the optimal control is obtained.The application can compensate system uncertainty, suppress various network errors and external disturbances, maximize trajectory tracking accuracy under the premise of ensuring that the joints of the robot arm do not exceed the limit and the body does not collide, effectively improve the robustness, stability and comprehensive performance of the control system.

The invention relates to the technical field of network error monitoring, in particular to a method for monitoring and analyzing network NOSOCKET errors, which comprises the following steps that: a user mode program initializes two eBPF programs and a storage structure; the two eBPF programs are mounted to the socket release function and the skb release function respectively; the first eBPF program reports the sock event entry to a user mode program; the user mode program stores the data in a storage structure; the user mode program carries out aging processing on the sock event entry; when the kernel calls the skb release function and the second eBPF program judges that the reason is NOSOCKET error, the second eBPF program reports the skb event entry to the user mode program; and the user state program searches for the sk event entry from the storage structure based on the skb event entry and obtains error processing information so as to realize monitoring and analysis of the NOSOCKET error.

This invention discloses an adaptive network data acquisition method and system based on error detection, belonging to the field of power distribution communication network operation monitoring and data acquisition technology. The method includes: acquiring error detection results based on a preset multi-error detection algorithm and pre-acquired power distribution network data; triggering frequency adjustment when a network error is detected; acquiring the number of errors based on a preset sliding time window and the error detection results; acquiring the error severity based on the number of errors and a preset anomaly threshold; acquiring a target acquisition frequency based on preset frequency adjustment parameters when a preset frequency adjustment condition is met; updating the current acquisition frequency to the target acquisition frequency, and acquiring the current power distribution network data based on the target acquisition frequency, thus achieving adaptive network data acquisition. This invention discloses an adaptive network data acquisition method and system based on error detection, which can solve the technical problem of the lack of adaptive capability in existing acquisition strategies.

The application discloses a fusion processing system and method for multi-source heterogeneous data of a power system under a 5G network, relates to the technical field of fusion processing of multi-source heterogeneous data of a power system, and comprises the following steps: collecting adjacent node phase angles, active power, voltage and time deviation on a unified time axis to generate observation paired phase angle difference and relative time deviation estimation; obtaining an electrical expected phase angle difference according to a small-angle power flow approximation, and mapping the relative time deviation to a 5G network projection phase angle according to a power frequency; performing difference on the three and normalizing the difference according to a noise equivalent standard deviation to obtain an inconsistency, complete paired network correction and generate a weight matrix; performing weighted least squares under the participation of the weight to obtain a state vector, and performing consistency review by residual error measurement to realize reliable fusion calculation under time delay fluctuation conditions. The application can separate network errors, and improve fusion precision and robustness.

This invention discloses a data processing method and system for fused terminals based on frame repair, applied in the field of intelligent communication technology. The method includes: detecting the integrity of received data frames and identifying incomplete data frames as damaged data frames; identifying the data type of the data segments of the damaged data frames based on their damage status, neighboring data frames, and a sample library, and determining whether the damaged data frames are repairable; repairing repairable damaged data frames according to their data type, verifying the repaired data segments using a checksum, and using the repaired damaged data frames as normal data frames. This invention achieves data frame segment repair, effectively reducing network error retransmissions, improving data transmission integrity and timeliness, and avoiding network channel congestion caused by network fluctuations, making it extremely applicable.

This invention discloses a method and apparatus for DOA estimation based on a spatially smoothed weighted sparse Bayesian algorithm. The method includes: reconstructing and normalizing the covariance matrix of the received signal from the array and the theoretical covariance matrix, and considering off-network error to construct a sparse reconstruction DOA estimation model under off-network conditions; obtaining a weight vector based on the spatially smoothed Capon algorithm and the theoretical covariance matrix; updating the parameters of the sparse Bayesian learning in the sparse reconstruction DOA estimation model according to the weight vector to obtain the first parameter; performing adaptive grid refinement based on the first parameter and the bipartite grid interpolation method, and iteratively updating the parameters of the sparse Bayesian learning; ending the iteration when a preset convergence condition is met, and outputting the DOA estimate.

A routing table of a network routing device is dynamically updated responsive to detecting a downstream error in a switch. The error can be identified by inspecting payloads appended to data packets received from downstream devices, to detect a deviation from the routing table. As a result, subsequent downstream packets use the alternative path to bypass the downstream error, until resolved.

Duplicate IP addresses within a network can lead to misdelivered packets or network outages. To mitigate routing errors, routers may enter an error mode when a duplicate IP address is detected, such as when a router advertising a route to a particular IP address receives an advertisement from another router for the same IP address. While operating in the error mode, the router updates packets to / from the duplicate IP address with a port number that satisfies a port criterion. When the router receives a packet addressed to the duplicate IP address, the router determines whether the destination port number satisfies the criterion. If the port number satisfies the criterion, the router routes the packet to a first host associated with the IP address. Otherwise, the router forwards the packet to the second router for delivery to a second host associated with the (same) IP address.

Methods, systems, and devices for method for wireless communication are described. A user equipment (UE) may receive, from a first network entity, a request to report one or more network configuration parameters associated with monitoring for a potential network misconfiguration of one or more network functions at the UE. The UE may further receive, from a serving cell or from a neighbor cell, control signaling indicating the one or more network configuration parameters associated with the one or more network functions at the UE. In some examples, the UE may transmit, to a second network entity in response to the request, a report indicating the one or more network configuration parameters. The UE may then receive, from the first network entity and based on the report, at least one updated network configuration parameter corresponding to at least one network function of the one or more network functions.

Methods and systems for automated detection of credential theft and network errors using channel change sequence entropy metrics. A method includes determining, by an automated channel change sequence detection system using an entropy-based method, a channel diversity value for channel change sequence data collected for a unique entry-point for streaming content from a service provider system, where the channel diversity value is a measure of the diversity of the channel change sequence data, reviewing, by the automated channel change sequence detection system using machine learning, at least the channel diversity value to determine an issue, and outputting, by the automated channel change sequence detection system to a service provider system component associated with the determined issue, an issue message to act on the determined issue.

A system for mitigating network error conditions in a multi-tenant environment is disclosed. The system transmits a policy request message to a server network function. The policy request message may include a tenant identifier (ID) and a network slice ID associated with a client device from which a network resource request is received. In response, the system receives a hypertext transfer protocol version 2 (HTTP / 2) status code from the server network function. The system determines a type of the HTTP / 2 status code, where the type of the HTTP2 status code indicates a type of condition. The system executes mitigation actions associated with the HTTP / 2 status code. The mitigation actions are customized based on the tenant ID, slice ID, or the type of the HTTP2 status error code. The system determines and outputs a result of the one or more mitigation actions.