A method and apparatus for performing disk management of an all flash array (AFA) server are provided. The method may include: utilizing a disk manager module among multiple program modules running on any node of multiple nodes of the AFA server to trigger a hardware layer of the any node to perform disk switching control in HA architecture of the AFA server, for controlling the any node to enable a set of disk switching paths between the any node and a group of storage devices among multiple sets of disk switching paths between the multiple nodes and multiple groups of storage devices; and utilizing the disk manager module to perform multiple groups of operations respectively corresponding to multiple disk pools in a parallel processing manner, for managing the group of storage devices with the multiple disk pools, wherein the multiple disk pools may include active, inserted, and failure disk pools.

Methods and systems for structuring, storing and displaying time series data in a user interface. One system includes processors executing instructions to determine, from time series data from a first sensor, a first subset of time series data for the first batch from the first start time and the first end time, determine, from the time series data from the first sensor, a second subset of time series data for the second batch from the second start time and the second end time, generate a time series user interface comprising a chart, the chart including a first plot for the first subset of time series data and a second plot for the second subset of time series data, the first plot being aligned to the second plot, and cause presentation of the time series user interface.

A method includes receiving, at an artificial intelligence (AI) accelerator of a computing system, at least one of: manufacturer data, third-party data, sensor data, or primary usage data of a consumable in a primary device and performing an AI operation on at least one of: the manufacturer data, the third-party data, the sensor data, or the primary usage data at the AI accelerator of the computing system using an AI model. The method further includes determining a primary life expectancy of the consumable in the primary device at the AI accelerator in response to performing the AI operation.

A system, method and computer program product for operating a low-voltage Internet-of-Things sensor device. The method includes sensing of the temperature dependence at each voltage condition in addition to the actual temperature and voltage. A programmed machine learning model uses the information to decide when it is appropriate to test the device functionality and use the results of different tests to determine when the system should run synchronously or asynchronously through a machine learning predictive algorithm. Based on said one or more sensed operating conditions, the system uses the model to detect a mode of operation of said IoT device indicating IoT device meets an expected level of performance, or a mode indicating said IoT device is not operating according to the expected level of performance. Based on the detected operating condition, the IoT device automatically adapts its operation to ensure a desired level of IoT sensor device performance.

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises collecting data capable of being replicated from a computing device; detecting risks of the computing device, wherein detecting risks comprises detecting the computing device's surroundings, location, speed, and condition; initiating data replication on the computing device once the risks are determined to reach a predetermined threshold; and storing the replicated data within a cloud storage system using a 5G network.

The present disclosure provides a method and system for determining an abnormity of a detection device of natural gas. The method comprises: obtaining a first detection data set collected by the detection device of the natural gas, determining whether the detection device is abnormal based on the first detection data set and a first historical detection data set, and sending a determination result to a terminal. The first detection data set and the first historical detection data set include a composition of natural gas, a temperature of natural gas, and a pressure of natural gas.

A system and method for data filtering and transmission management are provided. In particular, disclosed is a method of transmission management for data acquired by a remote monitor having a sensor. The method comprises the steps of: defining an initial trend envelope having a window around a forecast trend gradient, the window defined by an initial upper bound and an initial lower bound; and processing a set of data points acquired by the sensor, to identify any data points outside the initial trend envelope. When a point is identified outside the initial trend envelope, the method: (i) transmits an event data packet to a central server; and (ii) identifies a subsequent trend envelope based on a trend gradient derived from a preceding set of data points, said preceding set of points including an identified point from the event data packet.

A quality check apparatus, a quality check method, and a quality check program can check the quality of input data output to a processing module. A device outputs the input data and first metadata indicating an attribute regarding the quality of the input data to the processing module. The quality check apparatus includes a first obtaining unit and a check unit. The first obtaining unit obtains the first metadata. The check unit checks the quality of the input data based on the first metadata.

A method includes receiving, from sensors, current trace data including current sensor values associated with producing products. The method further includes processing the current trace data to identify features of the current trace data and providing the features of the current trace data as input to a trained machine learning model that uses a hyperplane limit for product classification. The method further includes obtaining, from the trained machine learning model, outputs indicative of predictive data associated with the hyperplane limit and processing the predictive data and the hyperplane limit to determine: first products associated with a first product classification and second products associated with a second product classification based exclusively on the subset of the plurality of features; and third products associated with the first product classification or the second product classification based on an additional feature not within the subset.

Described is a system including a server rack comprising a plurality of components, a plurality of touch sensors respectively coupled with the plurality of components of the server rack, and a management system communicatively coupled to the server rack. The management system comprises one or more processors and one or more computer-readable storage media storing instructions, which, when executed by the one or more processors, are configured to cause the management system to perform a method. The method comprises receiving, from the server rack, an indication of a failed component of the plurality of components. The method further comprises receiving, from a first touch sensor of the plurality of touch sensors, a touch indication. The method further comprises transmitting, to the server rack, an indication of whether the first touch sensor is coupled to the failed component.