26results about "Error identification" patented technology

An aspect of behavior of an embedded system may be determined by (a) determining a baseline behavior of the embedded system from a sequence of patterns in real-time digital measurements extracted from the embedded system; (b) extracting, while the embedded system is operating, real-time digital measurements from the embedded system; (c) extracting features from the real-time digital measurements extracted from the embedded system while the embedded system was operating; and (d) determining the aspect of the behavior of the embedded system by analyzing the extracted features with respect to features of the baseline behavior determined.

Provided are a system and a method for maintaining task operation continuity. The system includes a server that communicatively coupled to a first computer device and a second computer device. The server is configured to monitor an operating status of each of the first computer device and the second computer device, in which each of the first computer device and the second computer device is at least deployed with a first task on Docker, and the first task is assigned to be run by the first computer device, and in response to determining that the first computer device cannot operate normally, assign the first task to the second computer device that is operating normally to run through a distributed architecture.

Noisy data parsed from raw data can be received. The noisy data indicates first transaction events determined to be noise in the raw data. Using the noisy data, a first detection model can be trained to assign anomaly event indicators to second transaction events. The first detection model can receive an anomaly record. The anomaly record can indicate at least a portion of anomalous transaction events identified in runtime data. The first detection mode can assign the anomaly event indicators to the anomalous transaction events. The anomaly event indicators can indicate levels of severity of the anomalous transaction events identified in the runtime data.

The invention discloses a management and education monitoring system based on deep learning, relates to the technical field of computers, and is used for solving the problem that a traditional management and education monitoring system usually depends on manual operation or simple automatic scripts and is difficult to deal with high-concurrency and high-reliability requirements in a complex linkage scene. Service actions are constructed into an atomic action graph, and graph rewriting and two-stage convergence batching are carried out in combination with a mutual exclusion matrix, so that the degree of parallelism is maximized, and resource contention is avoided; an idempotent token is generated by the plan fingerprint and only an outgoing box is added, so that first successful receipt reuse and at most one side effect semantics are realized; a main key set capable of being aligned is provided through rehearsal and a state transition diagram, so that the execution premise can be quickly verified; triple verification of an interface level, a state level and a side certificate level is taken as a decision mechanism, a side certificate is taken as a negative criterion, and loss can be stopped quickly when false success and late success occur.

A method comprises analyzing, using one or more machine learning algorithms, parameters corresponding to at least one microservice operation processed by a first instance of a microservice, and predicting, based at least in part on the analyzing, whether the at least one microservice operation is anomalous. The first instance of the microservice is designated as being in an anomalous state responsive to predicting that the at least one microservice operation is anomalous. One or more requests for the microservice are routed to a second instance of the microservice responsive to the anomalous state designation. The method further comprises causing logging of information corresponding to operation of the second instance of the microservice to be enabled at a designated level of granularity.

The invention relates to a method and a system for detecting a runtime error occurring during a data processing. According to the invention, each of two or more data processing devices processes the same input data in order to generate useful data for a receiver. In each case, the processing of the input data is monitored by one or more respective monitoring functions, the monitoring results of which form the basis for a calculation of data processing device-specific keys, which are used by all but one of the data processing devices to code via the respective self-calculated hash values via the self-calculated useful data. The data processing device which does not code the self-calculated hash value receives the coded hash values and decodes same on the basis of self-calculated reference keys which have been calculated under the assumption that the monitoring process carried out by the other data processing devices has not resulted in an error during the processing of the input data. The decoded hash values, the self-calculated hash value and the self-calculated useful data are sent, by said data processing device, to a receiver which checks the hash values against the useful data in order to detect a runtime error occurring during the data processing. The invention also relates to a computer program and to a machine-readable storage medium.

Systems and methods for automatically identifying and resolving problem instances in data service workloads are disclosed. In some embodiments, a disclosed method includes: identifying a problem instance for a workload associated with a plurality of data service platforms; determining, using at least one machine learning model, a problem solution based on the problem instance and a catalog of problem solutions; executing the problem solution including operations across the plurality of data service platforms; and recovering the workload in accordance with a determination that the problem instance is resolved by the problem solution.

A set of payload flip-flops receives an input instance of a payload, and outputs an output instance from which a first instance of an error check signal is generated. One or more error check flip-flops receive the first instance and output a second instance. The input payload instance is clocked into the payload flip-flops if a payload enable signal is asserted, and the first error-check signal instance is clocked into the error check flip-flops if an error check enable signal is asserted. The input payload instance is input to the set of payload flip-flops over two clock cycles, and the payload enable signal is asserted for the two clock cycles. The error check enable signal is asserted on the second cycle. The first instance of the error check signal is compared with the second instance and an error signal is asserted if they do not match.

The invention provides an embedded system crash prediction method and device based on multi-source dynamic perception, and the method comprises the steps: carrying out the stack boundary monitoring and / or interruption storm detection of a system, and obtaining a detection result; performing deadlock probability calculation and / or memory leak trend analysis on the system to obtain an analysis result; and performing risk assessment on the detection result and the analysis result to obtain a crash risk value, comparing the crash risk value with a preset threshold value, and when the crash risk value exceeds the preset threshold value, judging that the system has a crash risk. According to the method, stack boundary monitoring and / or interruption storm detection are / is carried out on the system, deadlock probability calculation and / or memory leakage trend analysis are / is carried out on the system, a more comprehensive detection result and an analysis result are obtained, risk assessment is carried out on the basis, and a crash risk value is obtained; and the crash risk value is compared with the preset threshold value to predict the system crash in advance, so that the problems that the misjudgment rate is high and the crash cannot be prevented in the prior art are solved.

An imaging apparatus includes an image sensor, a controller, and an output interface. The image sensor captures a subject image to generate imaging data. The controller performs repair processing to generate a moving image file by sequentially analyzing information used for reproduction of a moving image from a corrupted file, the moving image file being capable of reproducing the moving image indicated by the imaging data, the corrupted file having a defect corrupted from the moving image file. The output interface outputs information to be presented to a user. The controller outputs a part of the moving image via the output interface, in a period from start of the repair processing on the corrupted file to completion of the repair processing, based on analyzed information in the repair processing in execution, the part of the moving image corresponding to the analyzed information.

A performance anomaly is detected in relation to a first data center equipment. An AI model is trained based on a plurality of anomaly patterns associated with a plurality of data center equipment and respective remediation processes associated with the anomaly patterns, to determine a remediation process that can resolve the detected performance anomaly associated with the first data center equipment. Upon execution of a machine-learning algorithm, the AI model compares real-time performance indicators recorded for the first data center equipment with performance indicators of the anomaly patterns and determines a matching anomaly pattern. The AI model determines a particular remediation process associated with the matching anomaly pattern. The remediation process is then implemented to resolve the performance anomaly associated with the first data center equipment.

An AI model is trained based on a plurality of anomaly patterns associated with a data center equipment to predict a performance anomaly associated with the data center equipment. Upon execution of a machine-learning algorithm, the AI model compares real-time performance indicators associated with the data center equipment to historical performance indicators of the anomaly patterns and determines a matching anomaly pattern. The AI model identifies a performance anomaly associated with the matching anomaly pattern and predicts that the performance anomaly is expected to occur in relation to the data center equipment. A remediation method is then implemented to avoid the performance anomaly from occurring.

In order to provide a method for fault analysis in a processing plant, for example a painting plant, by means of which fault situations can be easily and reliably analysed, according to the invention said method comprises the following: identifying, in particular automatically, a fault situation in the processing plant (101); storing, in a fault database (136), a fault situation data record for each fault situation that has been identified; automatically determining a fault cause for the fault situation, and / or automatically determining process values relevant to the fault situation, on the basis of the fault data record for the respective fault situation that has been identified.

A memory stores management information where identification information of each of a plurality of devices used by an information processing apparatus, first positional information, and second positional information are associated with one another. The first positional information indicates a position of a device storage storing the plurality of devices. The second positional information indicates a storage position of each of the plurality of devices in the device storage. A processor receives failed device information including identification information of a failed device among the plurality of devices from the information processing apparatus. The processor identifies the position of the device storage storing the failed device and the storage position of the failed device in the device storage from the identification information of the failed device included in the failed device information on the basis of management information.

Systems and methods for simulating cyber-physical systems are disclosed. A plurality of geographic simulation layers representing respective infrastructure sectors of a real-world environment may be generated, and the layers may be linked together with one another to create a multi-layer simulation. The associations between the layers of the simulation may be adjusted, and characteristics of the simulation layers themselves may be adjusted, to ensure that the simulation conforms to characteristics of the real-world environment being simulated. In some embodiments, a multi-user simulation system allows users at separate terminals to execute attack inputs and defense inputs against the simulation to try to destabilize and stabilize the simulation, respectively. Results of the attack inputs and defense inputs may be simultaneously displayed on a plurality of terminals.

Architectures and techniques are described that can leverage a container storage interface (CSI) driver to initiate volume health tests for a storage system in accordance with certain embodiments of this disclosure. The CSI driver can conform to a CSI specification or standard and can be used to expose the storage system to a container orchestration system. The volume health tests can be tailored to reduce or mitigate the impact of testing based on a status or state of the volumes. For instance, enforcement can be provided to ensure that only shorter, low-impact testing is performed on volumes having a published state, whereas longer, more intensive testing can be performed on volumes that are not in the published state.

The present disclosure provides an agent capability description method, a task execution method, an apparatus and an agent, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, large model, human-computer interaction, intelligent education, video generation and the like. The agent capability description method comprises: obtaining a task example for a to-be-tested agent; detecting, by a master agent, an example execution result to obtain an example detection result, the example execution result being determined based on the to-be-tested agent executing the task example, and the example detection result representing a matching degree between the example execution result and a task requirement condition for the task example; and describing, by the master agent, a task execution capability of the to-be-tested agent based on the example detection result and the task example to obtain capability description information for the to-be-tested agent.

The embodiment of the invention provides a system for maintaining task operation continuity, and the system comprises a server which is in communication coupling with a first computer device and a second computer device. The server is configured to: monitor individual operating states of a first computer device and a second computer device, where the first computer device and the second computer device are individually deployed with at least a first task in a Docker form, and the first task is assigned to be run by the first computer device; and in response to determining that the first computer device cannot normally operate, assigning the first task to a normally operating second computer device to operate through the distributed architecture. Therefore, the task can be transferred to another computer device which normally runs to continue to run through the distributed architecture, so that the effect of maintaining the continuity of task operation is achieved. The invention further relates to a method for maintaining task operation continuity.

The application discloses a deep learning-based management and control monitoring system and relates to the technical field of computers, which is used to solve the problem that traditional management and control monitoring systems usually rely on manual operation or simple automatic scripts and are difficult to cope with high concurrency and high reliability requirements in complex linkage scenarios; business actions are constructed into atomic action graphs, and graph rewriting and two-stage convergence batching are performed in combination with mutual exclusion matrices, so that parallelism is maximized and resource contention is avoided; an idempotent token is generated based on a plan fingerprint, and a send-only additional box is used to realize first-time successful reply reuse and at most once side effect semantics; through pre-rehearsal and state transition graphs, a set of main keys that can be aligned is provided, so that execution prerequisites can be quickly verified; three verification levels, i.e., an interface level, a state level and a side certificate level, are used as a decision mechanism, and the side certificate is a veto criterion, so that stop loss can be quickly performed when false success or late success occurs.

Methods and systems for managing operation of a deployment comprising data processing systems are disclosed. The operation may be managed by optimizing a diagnostic approach and / or a solution for an issue of an operation by a data processing system. The diagnostic approach may be optimized by using a trained inference model and / or at least one similarity map to identify at least one other data processing system. The at least one other data processing system may include contextual data for the issue. The data processing system may provide the issue and / or the contextual data to a large language model and / or a tree of thought model. The data processing system may use the tree of thought model to generate a remediative procedure for the issue.

Architectures and techniques are described that can leverage a container storage interface (CSI) driver to initiate volume health tests for a storage system in accordance with certain embodiments of this disclosure. The CSI driver can conform to a CSI specification or standard and can be used to expose the storage system to a container orchestration system. The volume health tests can be tailored to reduce or mitigate the impact of testing based on a status or state of the volumes. For instance, enforcement can be provided to ensure that only shorter, low-impact testing is performed on volumes having a published state, whereas longer, more intensive testing can be performed on volumes that are not in the published state.

Noisy data parsed from raw data can be received. The noisy data indicates first transaction events determined to be noise in the raw data. Using the noisy data, a first detection model can be trained to assign anomaly event indicators to second transaction events. The first detection model can receive an anomaly record. The anomaly record can indicate at least a portion of anomalous transaction events identified in runtime data. The first detection mode can assign the anomaly event indicators to the anomalous transaction events. The anomaly event indicators can indicate levels of severity of the anomalous transaction events identified in the runtime data.

The invention relates to a method and a system for detecting a runtime error that has occurred during data processing. It is designed that two or more data processing units (DPUs) each process identical input data to generate payload data for a receiver. The processing of the input data is monitored by one or more monitoring functions. Based on the monitoring results, DPU-specific keys are calculated. These keys are used by all but one of the DPUs to encode the payload data using their own self-calculated hash values. The DPU that does not encode its own hash value receives the encoded hash values ​​and decodes them using its own reference keys. These reference keys are calculated under the assumption that the monitoring of the other DPUs has not detected any errors in the processing of the input data.The decoded hash values, the self-calculated hash value, and the self-calculated payload are sent by this data processing unit to a receiver, which checks the hash values ​​against the payload to detect any runtime errors that occurred during data processing. The invention further relates to a computer program and a machine-readable storage medium.

The application discloses a consensus timeout response method and device in a consortium chain. One master copy node participating in consensus processing and a plurality of slave copy nodes with consensus states are determined, and the master copy node and the plurality of slave copy nodes are located in the same view. A challenge message of the master copy node responding to the consensus request timeout broadcast by the slave copy node in the view is collected, and the number of the slave copy nodes broadcasting the challenge message is counted. All the challenge messages are spliced into a challenge proof in the order of the serial identification codes of the slave copy nodes broadcasting the challenge messages. A confirmation message generated by responding to the challenge message broadcast by the slave copy node in the view is collected, and the number of the slave copy nodes broadcasting the confirmation message is counted. All the confirmation messages are spliced into a confirmation proof in the order of the serial identification codes of the slave copy nodes broadcasting the confirmation messages. Whether the master copy node is actually responding to the consensus request timeout is quickly judged according to the challenge proof and the confirmation proof.

In a hybrid cloud environment, a faulty job is identified and fault recovery is conducted under integrated control of storage set up at multiple sites. A system manages storage set up at multiple sites, using a low-level API for each set of storage, and has a high-level API integrally controlling the low-level APIs. The system includes an API interdependence data generation unit generating interdependence data describing API interdependence-related information upon calling of a low-level API, depending on low-level API use status, a job interdependence data generation unit that generates interdependence meta data upon successful job execution by the high-level API, and a fault identification unit that identifies a fault of the low-level API upon failure of the high-level API to execute a job, by using an interdependence data structure generated by the API interdependence data generation unit and the interdependence meta data generated by the job interdependence data generation unit.

The invention relates to a computer-implemented method of executing a main application (32 - 37) on a computer system by processing data, wherein - a first data processing unit (12) of a main computing unit (10) executes the main application (32 - 37) and - a safety application is executed on the computer system for monitoring the execution of the main application (32 - 37), wherein the safety application monitors an actual timing behaviour of the execution of the main application (32 - 37) and compares the actual timing behaviour with information about an expected timing behaviour and determines, based on a predefined criterion, if there is a significant deviation between the actual timing behaviour and the expected timing behaviour, and wherein, if the safety application has determined that there is a significant deviation, the safety application triggers an action that has been assigned to the significant deviation in advance. The safety application is executed on a second data processing unit (18) of a module (16) of the computer system, which second data processing unit (18) executes the safety application independently from data processing of the first data processing unit (12) and which module (16) is connected to the main computing unit (10) via a data transfer interface, and actual timing information, that is information about the actual timing behaviour and / or information that allows for determining the actual timing behaviour, is transferred via the data transfer interface to the second data processing unit (18).