Storage resource usage amount(s) are determined. For instance, storage resource usage data in a historical period related to a user is acquired. The pattern information of the storage resource usage data is determined according to a time series of the storage resource usage data, the time series being a series of observed values of the storage resource usage data in the historical period. In addition, the storage resource usage amount for a target period of the user can be determined based on the pattern information and the storage resource usage data. The pattern information at least includes at least one of a trend pattern, a cycle pattern, or an irregular pattern. Beneficially, a storage resource usage amount of a user can be more accurately determined in a future period, thereby providing the user with valuable reference information.

Storage resource usage amount(s) are determined. For instance, storage resource usage data in a historical period related to a user is acquired. The pattern information of the storage resource usage data is determined according to a time series of the storage resource usage data, the time series being a series of observed values of the storage resource usage data in the historical period. In addition, the storage resource usage amount for a target period of the user can be determined based on the pattern information and the storage resource usage data. The pattern information at least includes at least one of a trend pattern, a cycle pattern, or an irregular pattern. Beneficially, a storage resource usage amount of a user can be more accurately determined in a future period, thereby providing the user with valuable reference information.

A device is provided. The device includes processing circuitry configured to detect an audio signal associated with an alarm event, generate a spectrogram using a plurality of input samples of the audio signal where the spectrogram includes a plurality of spectral bins, select one of the plurality of spectral bins associated with a predefined frequency range, perform edge detection on the selected one of the plurality of spectral bins, perform pattern detection based on the edge detection, determine the audio signal corresponds to an audio alarm signal based on the pattern detection, and trigger an action in response to determining that the audio signal corresponds to an audio alarm signal.

A device is provided. The device includes processing circuitry configured to detect an audio signal associated with an alarm event, generate a spectrogram using a plurality of input samples of the audio signal where the spectrogram includes a plurality of spectral bins, select one of the plurality of spectral bins associated with a predefined frequency range, perform edge detection on the selected one of the plurality of spectral bins, perform pattern detection based on the edge detection, determine the audio signal corresponds to an audio alarm signal based on the pattern detection, and trigger an action in response to determining that the audio signal corresponds to an audio alarm signal.

Disclosed is a method for siting heat wave monitoring stations based on risk evaluation, including: acquiring historical meteorological data of a target region, and preprocessing the historical meteorological data to generate a gridded associated meteorological data set; identifying historical high-temperature heat wave events based on the associated meteorological data set, and calculating parameters and summary indexes of heat wave feature of grids; evaluating station building priority of the grids based on spatial distribution features of the summary indexes; acquiring multi-source data, and evaluating a heat wave risk to generate a heat wave risk map; performing iterative computation using an optimization algorithm based on current station building information, temporal-spatial distribution features of meteorological factors and the heat wave risk map to determine alternative station building positions; and acquiring on-site survey information of each alternative station building position, and determining a position where a station is to be built.

Disclosed is a method for siting heat wave monitoring stations based on risk evaluation, including: acquiring historical meteorological data of a target region, and preprocessing the historical meteorological data to generate a gridded associated meteorological data set; identifying historical high-temperature heat wave events based on the associated meteorological data set, and calculating parameters and summary indexes of heat wave feature of grids; evaluating station building priority of the grids based on spatial distribution features of the summary indexes; acquiring multi-source data, and evaluating a heat wave risk to generate a heat wave risk map; performing iterative computation using an optimization algorithm based on current station building information, temporal-spatial distribution features of meteorological factors and the heat wave risk map to determine alternative station building positions; and acquiring on-site survey information of each alternative station building position, and determining a position where a station is to be built.

A computer program product is tangibly embodied on a non-transitory computer-readable medium and includes instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to input a situation event graph, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph into a neural network model. The neural network model includes a plurality of scenarios received from a database, where the situation event graph represents a situation and each of the plurality of scenarios represents at least two similar situations. The neural network model processes the situation event graph, the topology data, and the knowledge graph to determine a similarity estimate between the situation event graph and the plurality of scenarios. The situation event graph is identified as a match to one of the plurality of scenarios based on the similarity estimate.

A computer program product is tangibly embodied on a non-transitory computer-readable medium and includes instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to input a situation event graph, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph into a neural network model. The neural network model includes a plurality of scenarios received from a database, where the situation event graph represents a situation and each of the plurality of scenarios represents at least two similar situations. The neural network model processes the situation event graph, the topology data, and the knowledge graph to determine a similarity estimate between the situation event graph and the plurality of scenarios. The situation event graph is identified as a match to one of the plurality of scenarios based on the similarity estimate.

Embodiments include methods for identifying communications network performance management (PM) data that is explanatory of prediction target information. Such methods include obtaining a time series of PM data representing performance of the communication network at a plurality of periodic time instances over a first duration, and based on the time series of PM data, computing a plurality of models representing a corresponding plurality of statistical characteristics of the time series of PM data. Such methods also include computing projections of the models onto the time series of PM data, and based on the projections, selecting one or more of the models that are most explanatory of the prediction target information. Various examples of time series of PM data and target information are disclosed. Other embodiments include computing apparatus configured to perform such methods.

Embodiments include methods for identifying communications network performance management (PM) data that is explanatory of prediction target information. Such methods include obtaining a time series of PM data representing performance of the communication network at a plurality of periodic time instances over a first duration, and based on the time series of PM data, computing a plurality of models representing a corresponding plurality of statistical characteristics of the time series of PM data. Such methods also include computing projections of the models onto the time series of PM data, and based on the projections, selecting one or more of the models that are most explanatory of the prediction target information. Various examples of time series of PM data and target information are disclosed. Other embodiments include computing apparatus configured to perform such methods.