516results about "Fault response" patented technology

The application relates to the technical field of memory detection, in particular to a memory usage tracking method and a storage medium. The method constructs a target memory block containing a header space and a data space through obtaining a memory allocation request, records memory management information in the header space, and hangs the target memory block to a ring-shaped bidirectional linked list; in a memory access process, the validity of the target memory block is checked and access time information is updated, and safe release is performed according to the memory management information when releasing; through periodic traversal of the ring-shaped bidirectional linked list, in combination with the time information and the reference state of the target memory block, a memory usage tracking result is generated, and continuous monitoring and abnormal occupation analysis of the memory life cycle are realized. The application realizes traceability, checkability and analysis of the memory usage state, thereby effectively improving the safety, reliability and problem positioning efficiency of memory management.

The present disclosure provides a problem positioning method and device, equipment and storage medium, relates to the technical field of computers, in particular to problem positioning technology. The specific implementation scheme is: obtaining a natural language input, the natural language input is used to describe an abnormal problem of application runtime; the natural language input is analyzed to obtain an analysis result, the analysis result contains problem semantic information corresponding to the abnormal problem; based on the analysis result, an operation and maintenance knowledge segment corresponding to the problem semantic information is retrieved from a pre-constructed knowledge base; based on the analysis result and the retrieved operation and maintenance knowledge segment, target log data is obtained; based on the retrieved operation and maintenance knowledge segment and the target log data, a problem positioning result is generated. The method realizes the automatic processing of the natural language description problem, and improves the efficiency and accuracy of operation and maintenance problem positioning through the collaborative analysis of the knowledge base and the log data.

In some embodiments, techniques are provided for analyzing time series data to detect anomalies. In some embodiments, a machine learning model is used to process time series data. In some embodiments, the machine learning model is trained in an unsupervised manner on a large amount of prior time series data, allowing a highly accurate model to be created from new data. In some embodiments, the training of the machine learning model alternates between a fit optimization and a prune optimization to allow a large amount of training data to be processed that includes unlabeled anomaly records. Because a machine learning model is used, anomalies within complex systems can be detected, including but not limited to autonomous vehicles such as unmanned aerial vehicles. When an anomaly is detected, a command can be sent to the monitored system (e.g., autonomous vehicle) to address the anomaly.

The present application relates to the technical field of log analysis, and particularly relates to a kind of audit system operation and maintenance log automatic generation method and equipment.The method parses log stream to distinguish business log entries from unassociated abnormal entries, constructs thread execution sequence and calculates the idle time slice between adjacent business log entries in thread execution sequence;Load the resource constraint mapping table defining hardware resource type and minimum duration;Get the candidate business entries of unassociated abnormal entries, and perform mutual exclusivity and capacity physical verification using the resource constraint mapping table to filter out effective candidate business entries;According to the time difference between unassociated abnormal entries and its effective candidate business entries, filter out the best matching business entries, and then backtrack the business context, inject the exception stack and correct the state to generate structured audit records.The present application realizes non-invasive and high-precision exception attribution based on the rules of runtime resource constraints, and improves the integrity and accuracy of audit data.

An integrated circuit (IC) chip and / or system includes a processing core and an input / output (I / O) controller, coupled to a host interface, to detect a plurality of state transitions in a data signal received over a system management bus (SMBUS) of the host interface and generate an interrupt responsive to detecting each state transition. An interrupt controller routes each interrupt to the processing core. The processing core, in response to receipt of each respective interrupt, retrieves a corresponding state transition received over the SMBUS and stores, in a memory, the corresponding state transition.

A universal asynchronous receiver / transmitter includes a transmit register including information to be transmitted, a receive register including received information, a frame error check circuit that evaluates contents of the receive register for a frame error, and a control logic. The control logic routes contents of the transmit register to the receive register. During transmission of the contents of the transmit register to the receive register through a reprogrammable pin, the control logic modifies a bit inversion register to produce modified contents to be provided to the receive register. The modified contents cause a frame error. The control logic determines whether the frame error check circuit detects the frame error.

This invention discloses a method, device, and medium for error prediction and self-repair in low-code applications, belonging to the field of low-code platform technology. It addresses the technical problems of existing low-code platforms, which rely heavily on manual testing, are prone to lag, have blind spots, and are difficult for non-professional developers to diagnose deep-seated logical errors. The method includes: classifying and configuring business scenarios in the low-code platform, collecting and obtaining scenario constraint data; performing error prediction detection on the low-code data at both dynamic and static levels to obtain dynamic and static error results; performing error prediction detection on the low-code data at the machine learning prediction layer to obtain machine learning layer error prediction results; classifying and repairing both dynamic / static error results and machine learning layer error prediction results to obtain repair result information; and blocking high-risk error results to obtain blocking result information; thereby completing error prediction and self-repair in the low-code application.

Provided are techniques for a troubleshooting system. Selection of a trace is received. The trace is associated with raw log lines. A trace category of the trace is identified, where the trace category is associated with a common log lines template. Common log lines are generated by removing log lines from the raw log lines that do not appear in the common log lines template. One or more relevant trace categories and one or more relevant traces are identified. A problem is identified using the trace, the common log lines, the one or more relevant trace categories, and the one or more relevant traces. A solution for the problem is implemented.

This invention discloses a real-time data anomaly monitoring and repair method based on machine learning, comprising the following steps: S1, constructing data fragments and generating a consistent constraint set; S2, performing self-consistent representation construction; S3, performing anomaly detection and generating anomaly label set; S4, performing masking processing to generate masked data fragments, inputting them into an improved UniTS model, introducing a structural perturbation suppression mechanism, and generating a repair candidate set; S5, performing structural consistency evaluation and screening target repair data that meets the minimum structural perturbation condition; S6, performing data repair and generating repaired data fragments; S7, feeding back the updated consistent constraint set to the improved UniTS model. This invention achieves highly sensitive real-time identification of anomalous data items, effectively reducing the anomaly false judgment rate and repair distortion rate, and improving the data security, stability, continuity, and reliability in high-concurrency data stream environments.

The application discloses a kind of power system container operation state information identification and fault detection method and system, comprising: definition includes system identification, container group, container type, scene, container number Container naming rule;Process management, container state management and sub-scene state management model are constructed;Container image is constructed, and container host name resolution engine and process management configuration are preset;By self-defined configuration, the container name, environment variable and container number that meet the container naming rule are set;When container starts, container name is resolved by host name resolution engine, identifies the scene, sub-scene and container number of the container belonging to, and determines sub-scene instance number;Process management calculates the container health index according to the sub-scene process health index in container, triggers container fault, and triggers corresponding fault handling by cascading to host computer sub-scene.The problems of non-standard container identification, low scene deployment flexibility, fault response lag and insufficient full-link association in the prior art are solved.

This cause isolation system for isolating a cause of an occurrence of a detected event manages a path formation determination graph in which, among components constituting a target system, components to which an abnormality may spread due to a prescribed cause are set as nodes and the nodes are connected by edges. The cause isolation system associates an abnormal event pertaining to the abnormality extracted from a detected event with the nodes related to the abnormal event in the path formation determination graph, and performs path formation determination for determining whether a prescribed path is formed by the associated abnormal event in the path formation determination graph. On the basis of the determination result of the path formation determination, the cause isolation system assesses the possibility that the cause of the occurrence of the detected event is the prescribed cause.

The application belongs to the technical field of learning software performance monitoring, and relates to a terminal learning software running performance monitoring and abnormality identification system. The system captures switching instructions between internal functional units of the learning software by monitoring a terminal message bus, determines whether switching performance abnormality occurs according to end-to-end time consumption of switching, opens a concurrent monitoring window in response to the switching performance abnormality, traces system resource occupation object properties generated by a kernel in full, gives identity tags to memory objects, file handles, network sockets and active threads, extracts resource real-time sampling values carrying the identity tags, quantitatively analyzes resource occupation overlapping situations and change rules in a resource release process of a preceding unit and a resource loading process of a subsequent unit, identifies a root cause of the switching performance abnormality and outputs a report. The application can trace abnormal root causes in a learning software switching delay state, and helps improve software running stability.

Embodiments of the present disclosure relate to a method, apparatus, device, medium and program product for detecting a page. The method presented herein includes: obtaining multi-modal information of a target page to be detected, wherein the multi-modal information is determined based on multiple types of data of the target page, and the multi-modal information indicates content presented by the target page after performing a predetermined operation; determining availability of at least one function of the target page based on the multi-modal information and a reference page associated with the predetermined operation; and determining whether there is an abnormality in the target page after performing the predetermined operation based at least on the determination result of the availability.

The application discloses a fault diagnosis function version iteration method and device, equipment and storage medium. The method comprises the following steps: acquiring current fault diagnosis code corresponding to a current fault diagnosis function version; comparing and processing the current fault diagnosis code with historical fault diagnosis code corresponding to a historical fault diagnosis function version, so as to determine whether the underlying software code and the application diagnosis code are changed; in the case that the underlying software code is changed and the application diagnosis code is not changed, based on an automatic cutting script executor, the target fault diagnosis function version is determined according to the historical fault diagnosis code corresponding to the historical fault diagnosis function version and the current fault diagnosis code, so that the target fault diagnosis function version is automatically iterated and determined in the case that the underlying software code is changed and the application diagnosis code is not changed, the operation process is simplified, and the overall development iteration efficiency is improved.

Embodiments of the present application provide a program error tracing method, electronic equipment, storage medium and program product, the method comprises: obtaining behavior data of program runtime; compare the behavior data with the benchmark behavior model, identify the abnormal behavior node; based on the abnormal behavior node, construct the correlation graph of program behavior; based on the correlation graph, the root cause of the program error is located by bidirectional tracing. The change from experience-dependent, isolated phenomenon analysis to systematic, automatic root cause mining is realized, and the cross-module, implicit complex error tracing problem is solved.

Provide a system. 【Solution means】 Means for receiving inquiry information, Means for analyzing the received inquiry to identify problems, Means for determining automatic countermeasures for problems, Means for executing the determined countermeasures, Means for monitoring the system state after execution, Means for escalating to human operation personnel as necessary, Means for visualizing the operation status of the system in real time through physical sensors, Means for providing the visualized information to a display device, A system including the above.

The invention relates to a device and a method for machine learning with an artificial neural network, characterized in that a layer description is provided for a computing hardware (110) for an artificial neural network, which layer description defines at least a part of a layer of the artificial neural network, wherein the layer description defines a tensor of input values of the at least a part of the layer, a tensor of weights of the at least a part of the layer and a tensor of output values of the at least a part of the layer, in particular initial addresses of these tensors, wherein the computing hardware (110) sends a message (216) for the transmission of the input values, which comprises the initial address of the tensor of the input values in a memory (102), wherein the computing hardware (110) sends a message (224) for the transmission of the weights, which comprises the initial address of the tensor of the weights in the memory (102), or wherein the computing hardware (110) sends a message (232) for the transmission of the output values, which comprises the initial address of the tensor of the output values in the memory (102).

A fault prediction method, device, equipment, medium and product are disclosed. The method comprises: obtaining a target data set, the target data set comprising: a container running index, an application log text, a listening event and a link tracking index; performing fault prediction based on the target data set to obtain a fault prediction result, the fault prediction result comprising: a fault probability, a fault type and a prediction time. Through the technical solution of the present application, real-time analysis of multi-dimensional indexes during container running can be performed to realize early prediction of faults.

This invention discloses a data loopback error analysis method, apparatus, device, and readable storage medium, applied in the field of data communication technology. The method includes: acquiring a count value in a setting register determined based on a preset number of target identifiers; wherein the setting register records the number of consecutive loopbacks of the preset number of target identifiers; the target identifiers are predetermined values ​​that are not scrambled during transmission at a set rate; and analyzing errors in the data loopback process based on the count value. Compared to current methods where valid information cannot be directly obtained from the device side when an error occurs in the data loopback function, this invention records the number of consecutive loopbacks of the preset number of target identifiers in a register. Therefore, based on the count value in the register, problems when errors occur in the loopback state can be quickly analyzed, errors can be quickly located, and error analysis and location efficiency can be improved.

The application discloses a bus fault reason determination method and electronic equipment, and relates to the technical field of computers, and comprises the following steps: if a target bus in a controller fails, determining a source pin corresponding to the target bus; determining a target pin corresponding to the source pin from a reserved pin; configuring the target pin based on configuration information of the target bus; configuring an interrupt mode of the source pin as an edge-triggered interrupt mode; when the source pin triggers an interrupt, reading a level signal of the source pin, setting an output level signal of the target pin to be consistent with the level signal of the corresponding source pin; and determining a fault reason of the target bus by collecting the output level signal of the target pin through a reserved terminal corresponding to the target pin by using a logic analyzer. The level signal of the source pin is copied to the target pin, and the logic analyzer is connected to the reserved terminal corresponding to the target pin, so that the technical effect of improving the fault reason determination efficiency is achieved.

Embodiments of the present disclosure relate to a coprocessor, a host processor, a crash detection method and an electronic device. In this method, the coprocessor sends a handshake request to the host processor capable of running an operating system. The host processor performs an operation triggered by the handshake request. The host processor sends a positive response to the handshake request to the coprocessor if the operation is performed successfully; and the host processor sends, to the coprocessor, a negative response to the handshake request or no response if the operation is performed successfully. The coprocessor monitors a response to the handshake request from the host processor and determines a detection result of hardware crash associated with the operating system at least based on a monitoring result of this response.

An electronic control unit (114) is presented. The electronic control unit (114) comprises an electronic device (118), an application controller (128), and a safety node (134). The application controller (128) is configured to control the electronic device (118). The safety node (134) is configured to monitor input signals sent to the electronic control unit (114) by a higher-level controller (112). The safety node (134) is further configured to identify a fault signal from the input signals. The safety node (134) is also configured to trigger a reset of the application controller (128) upon identification of the fault signal. A method for triggering a reset and a control system (110) are also presented.

The application discloses a computer fault alarm system and method, which is applied to the technical field of fault alarm and solves the technical problem of computer fault alarm. The computer fault alarm method comprises the following steps: (S1) collecting and storing computer fault information in a listener database through a fault management module; (S2) periodically deleting the computer fault information in the listener database on the basis of partitioning by a listener service process module; (S3) updating the computer fault information in an alarm database and an event database by a listener service program module, and transmitting the computer fault information to the listener database; and (S4) receiving fault alarm information output by the listener database by the fault management module, and controlling an alarm to send an alarm sound and light signal. The computer fault alarm system greatly improves the efficiency of processing computer fault information, and ensures that the error of the processing result of the computer fault information is less than 3%.

The application discloses an intelligent office multi-screen splicing display control and practical training system and method, comprising a multi-screen splicing topology configuration module, an intelligent display control scheduling module, a practical training scene simulation generation module, an interactive instruction analysis and execution module, a fault diagnosis and tracing module, a practical training data management and analysis module, and a safety protection and permission control module; the application realizes automatic identification of display equipment and generation of an optimal splicing scheme through the multi-screen splicing topology configuration module and an adaptability calculation algorithm, supports dynamic topology switching of office and practical training scenes, and solves the problem of poor adaptability of traditional splicing relying on manual configuration; based on task priority calculation and time-sharing scheduling algorithm, the application realizes intelligent resource allocation in a multi-task concurrent scene, guarantees the fluency and synchronization of multi-screen display, and improves the display control efficiency of intelligent office.

The application discloses a kind of UVM verification platform automatic generation system and method based on generative artificial intelligence, the system includes generative AI dynamic anchoring module, LLM-VCS self-feedback verification module and conflict detection correction module;The generative AI dynamic anchoring module is used to solve the context window limit problem on large language model, by the key component in UVM platform is marked as anchor file and generates subsequent file based on dependency relationship priority;LLM-VCS self-feedback verification module cooperates with large language model and EDA tool chain, forms closed loop system from specification input to UVM platform generation, simulation and debugging;The conflict detection correction module is used to detect and correct the interface inconsistency and logic error between generated files.The application can adapt to the verification needs of different RTL designs, effectively solve the interface consistency problem of high coupling between UVM modules, flexibly generate UVM platform of different structures, significantly improve the verification efficiency and quality.

Provided herein are techniques for developing fault-tolerant quantum computation (FTQC) systems by leveraging quantum error correction (QEC) strategies in a neutral atom quantum information processing system. Techniques describe herein leverage error correction methods to correct errors and replenish lost qubits during operation of the quantum computer. To implement such error correction strategies, the quantum information processing system described herein uses a combination of various syndrome extraction (SE) methods and selective-state readout (SSR). These SE methods enable high-fidelity quantum computing with reduced computational overhead.

This invention provides a root cause localization method, device, and medium based on a large model, relating to the field of data processing technology. The method determines initial abnormal nodes and their corresponding initial abnormal scores based on a fault call chain graph and abnormal indicator data. Based on the call chain path from the starting point of the call chain graph to the initial abnormal node, the node identifier of the initial abnormal node, abnormal indicator data, target abnormal logs, historical fault cases, and the large model, it determines target abnormal nodes and obtains their corresponding predicted abnormal scores and node selection description text. Combining the initial abnormal scores, predicted abnormal scores, and adjusted weights obtained based on the node selection description text, it obtains the target abnormal score of the target abnormal node and sorts the target abnormal nodes accordingly to obtain the root cause localization result. This method not only integrates fault call chain topology and quantitative indicator data but also effectively utilizes fault semantic information in unstructured text such as logs, improving the accuracy of root cause localization.

The application discloses a kind of methods for monitoring crash log, equipment, device and storage medium, the method comprises: if the application program of running exists exception is monitored, the exception data corresponding to the application program is obtained;Abnormal data is filtered and handled based on the exception type corresponding to the exception type, and the filtered exception data is stored. By quickly investigating the exception of application program, so that the developer more timely, accurate solution crash problem.

The invention relates to a code error feedback method combining static analysis and dynamic analysis, and mainly solves the problems of insufficient error feedback and low efficiency in an Online Judge system: reconstructing codes of correct and error versions, including alignment and renaming of variables, and rearranging statements on the premise of not influencing semantics; static and dynamic multi-level alignment is carried out, in the static alignment, an editing script with the minimum cost is calculated through a heuristic editing distance algorithm, an alignment block and a difference list are obtained, in the dynamic alignment, compiling execution is carried out on source codes of two versions, test cases which do not pass operation are dynamically printed through gdb, and execution track information of the test cases is dynamically printed through gdb; error positioning is achieved, data flow backtracking analysis and scoring are carried out on error positions, and error feedback is generated. The method has the characteristics of high execution efficiency, high accuracy and easier understanding of generated feedback, automatic feedback of error codes can be realized, and the learning efficiency of students in programming tasks is improved.