949 results about "Time segment" patented technology

Method comprising: receiving, for each of plural times, a respective first indication indicating that a first base station adopted a respective beam setting at the respective time; storing, in a database, for each of the plural times, the respective beam setting adopted, according to the respective first indication, by the first base station at the respective time along with the respective time; monitoring whether a request for the beam setting adopted by the first base station in a time period is received; retrieving, from the database, the one or more beam settings adopted, according to the database, by the first base station in the time period if the request is received; reporting the retrieved one or more beam settings adopted, according to the database, by the first base station in the time period in a response to the request.

The invention relates to the technical field of subway information broadcast control, and discloses a subway information broadcast control method based on time period scheduling. The method comprises the steps of collecting multi-source monitoring data of a subway operation environment in real time, wherein the multi-source monitoring data comprises passenger flow data, train arrival data and emergency event report data; dividing the current operation cycle into a plurality of continuous time slices based on a preset time period division rule, and generating a corresponding time period identification code for each time slice; matching a target broadcast control template from a broadcast control strategy library according to the time period identification code, and performing parameter dynamic filling on the target broadcast control template in combination with the multi-source monitoring data; and performing logic verification on the target broadcast control template after parameter filling, triggering a broadcast control decision reconstruction process when verification is abnormal, and generating an executable broadcast control instruction set when verification is normal. According to the method, operation requirements of different time periods are dynamically adapted, so that the accuracy and timeliness of subway information broadcast control are improved, and the passenger travel experience and the operation efficiency are optimized.

The invention discloses an airport flight quantity and throughput prediction method and system, and relates to the technical field of intelligent airport management. The method comprises the following steps: firstly, determining a future target time period to be predicted, and obtaining related multi-source data according to the future target time period; preprocessing the data and extracting time sequence and non-time sequence features; processing time sequence features by using the trained long-short-term memory neural network model, processing non-time sequence features by using the extreme gradient boosting tree model, and respectively obtaining prediction results; and integrating the outputs of the two types of models through a weighted fusion strategy to obtain an accurate prediction value of the flight sortie and the single-machine passenger capacity, and further calculating to obtain a passenger throughput prediction result. According to the method and the system provided by the invention, through the architecture of multi-source data fusion and hybrid intelligent model cooperation, the prediction precision and reliability are effectively improved, and scientific decision support is provided for airport operation management.

Provided in the embodiments of the present disclosure are a resource scheduling method and apparatus. The resource scheduling method comprises: determining historical operating log data and historical resource usage data of a data processing node set which are generated within a preset historical time period; on the basis of the historical operating log data, and by means of using a log prediction model, predicting predicted operating log data of the data processing node set that is generated within a preset future time period; on the basis of the predicted operating log data, and by means of using a resource prediction model, predicting predicted resource usage data of the data processing node set that is required within the preset future time period; on the basis of the historical resource usage data, determining target resource usage data of the data processing node set within the preset future time period; and on the basis of the predicted resource usage data and the target resource usage data, performing resource scheduling on the data processing node set. The resource scheduling method is more reasonable and more intelligent, and does not require human participation, thus resulting in more stable application systems.

The present application relates to the technical field of communications, and discloses a path planning method and apparatus, a device, a system, and a storage medium. The method comprises: acquiring traffic prediction information respectively corresponding to at least one time period; and on the basis of the traffic prediction information respectively corresponding to the at least one time period and at least one selectable path respectively corresponding to each service, determining a target forwarding path of each service meeting path planning objectives. The path planning objectives comprise: the service traffic borne by any link in a hierarchical network in any time period is less than or equal to the bandwidth capacity upper limit of any link, load balancing is realized between multiple links connected to a same network node in the hierarchical network, and the transmission quality of the target forwarding path of any service meets a quality requirement of any service. Path planning of a global service is realized, network link congestion can be reduced, and better effects that load balancing between all network links is realized and a service forwarding path meets a service quality requirement are achieved.

The invention discloses a power transmission line icing galloping prediction method and system based on an improved time sequence large model, equipment and a storage medium, and relates to the technical field of power system safety monitoring, and the method comprises the steps: obtaining line parameters, and meteorological data and power transmission line icing galloping data in an abnormal accumulation time period; constructing a data feature project by using the line parameters, the meteorological data in the abnormal accumulation time period and the icing galloping data of the power transmission line; performing data synthesis on the meteorological data and the icing galloping data of the power transmission line in the abnormal accumulation time period through time alignment to obtain a comprehensive time sequence; and taking the comprehensive time sequence as input, and obtaining the icing galloping probability and the galloping amplitude of the power transmission line based on the output of the improved time sequence large model. According to the invention, the icing galloping risk early warning of the power transmission line can be rapidly and accurately output, and reliable guarantee is provided for safe operation of a power grid.

The invention relates to the technical field of elevator energy consumption analysis, and discloses an elevator energy consumption analysis method and system.The elevator energy consumption analysis method comprises the steps that elevator operation data are obtained, anchor point events are detected based on the elevator operation data, time alignment processing is conducted on the elevator operation data, and a synchronous data sequence with a unified time axis is generated; based on the synchronous data sequence, elevator running states are recognized, regeneration running stages are marked, and time segments corresponding to the elevator running states are output; carrying out integral calculation on the active power of the line in a time segment, realizing bottom consumption stripping in combination with a standby power reference, and outputting segment energy consumption through positive and negative power decomposition; and in the regeneration operation stage, feedback energy and braking energy are calculated according to the elevator operation data, feedback efficiency is calculated, and an energy consumption analysis result is output. According to the method, a reliable basis is provided for energy efficiency evaluation, abnormal early warning and operation optimization of the elevator system.

The invention discloses a network equipment fault analysis method and device based on multi-dimensional characteristics, and relates to the technical field of network equipment fault analysis, and the method comprises the steps: obtaining the time sequence characteristic data of each network equipment in an Internet of Things cluster during the operation of a current time period, and forming a multi-dimensional network fault identification data set according to the time window dimension, the network equipment dimension and the feature dimension, carrying out encoding processing on the network fault identification data set by adopting a pre-trained encoder to obtain an encoding vector, inputting the encoding vector into a pre-constructed fault prediction decoder, and carrying out fault prediction on the network fault identification data set according to the pre-constructed fault prediction decoder. And outputting the fault probability of each network device in the preset time period. The network equipment fault diagnosis method and device solve the technical problems that when network equipment fault diagnosis is carried out in the related technology, due to the fact that network faults have space-time variability and diversity, single-dimension analysis cannot accurately capture the full view of the faults, and fault diagnosis is delayed and misdiagnosed.

The invention provides an elastic thread pool management method and device based on load prediction and electronic equipment, and the method comprises the steps: obtaining a task submitted by a client, and routing the task to a corresponding queue according to the task feature of the task; wherein the I / O type task is routed to an asynchronous queue, and the CPU calculation type task is routed to a thread pool queue; monitoring and collecting thread index data of the thread pool queue in real time, and obtaining thread index data of a first preset time period based on a preset sliding window; inputting the thread index data of the first preset time period into a prediction model to obtain a predicted load value of a second preset time period; wherein the prediction model is a Holt-Winters model, and the prediction model is a Holt-Winters model; and performing thread adjustment based on the predicted load value and a preset load threshold value. The problems existing in an existing thread pool management scheme are solved.

Certain aspects of the present disclosure provide flow estimation techniques for dynamic scenes. A method generally includes obtaining a first time series sequence of sample sets of a scene (including a first and a second set of samples) over a first period of time associated with a reference time; processing, with a spatial-temporal attention module, the first time series sequence, to generate a modified first time series sequence of sample sets of the scene comprising the first set and not the second set of samples; processing, with a first neural ordinary differential equation (ODE) and a first ODE solver, the modified first time series sequence to predict a second time series sequence of sample sets; and generating a first output indicating a predicted flow of the scene from the reference time to a first selected time based on the modified first time series sequence and the second time series sequence.

This application relates to the field of communication technologies, and discloses a communication method, apparatus, and system. The method includes: A terminal determines predicted values of first-type measurement quantities of a plurality of cells at at least one time point in a first time period, where the plurality of cells include a serving cell and a candidate cell; and if the predicted values of the first-type measurement quantities of the plurality of cells at the at least one time point satisfy a reporting condition, sends measurement report information to a network device.

The invention belongs to the technical field of behavior prediction, and particularly relates to a ship anomaly detection method and device based on TCN trajectory prediction. The method comprises the following steps: S1, acquiring AIS data given by a ship automatic identification system in a set time period before the current moment; s2, predicting the position of the ship at the next moment based on a pre-trained time convolutional network; s3, determining an error between the actual position and the predicted position of the ship at the next moment; s4, when the error exceeds a threshold value, determining that the position is an abnormal point; s5, continuously appearing abnormal points serve as abnormal track segments, the time length of the abnormal track segments serves as a weight calculation factor, and the abnormal scores of the abnormal track segments are calculated; and S6, determining the abnormal level of the abnormal track segment according to the abnormal score and the plurality of abnormal degree intervals. According to the invention, the detection precision of ship abnormal behaviors is improved.

The invention relates to the technical field of data analysis, in particular to an ultra-low temperature refrigerator based on operation data analysis, which comprises the steps of forming a fluctuation event sequence under multiple time periods by regarding a time period corresponding to temperature fluctuation as a fluctuation event; dividing the fluctuation event sequence into event types corresponding to fluctuation events according to values in an operation period, and calculating a fluctuation duration proportion under each time segment; defining temperature state transition of a plurality of fluctuation events under each fluctuation duration proportion, combining the fluctuation events according to a plurality of time periods, and solving a fluctuation event combination probability; performing correlation analysis on the fluctuation events by taking the event attributes and the temperature characteristics of the fluctuation events as input to obtain related factors of the combined fluctuation events; carrying out multi-objective solution according to related factors of the fluctuation event, solving an optimal control parameter combination, and taking the optimal control parameter combination as an output control strategy; the control efficiency of the refrigerator and the accuracy of temperature regulation and control are achieved.

The invention relates to the technical field of computer processing, in particular to a Kafka cluster configuration parameter adjusting method and device, equipment and a medium, and is used for relieving problems caused by a fixed change means of code modification. The method comprises the following steps: according to related historical data of a Kafka cluster, predicting a first predicted load index in a future time period through a first time sequence model, and predicting a second predicted load index in the future time period through a second time sequence model; weighting the first predicted load indicator; weighting the second predicted load index; determining a predicted load index according to the weighted first predicted load index and the weighted second predicted load index; determining a plurality of first candidate adjustment actions according to the predicted load index; selecting a first candidate adjustment action from the plurality of first candidate adjustment actions as a first adjustment action; and adjusting a partition allocation index and a copy allocation index of the Kafka cluster configuration parameters by using the first adjustment action.

The invention discloses an energy consumption prediction and optimization system and method based on artificial intelligence, and relates to the technical field of energy consumption prediction and optimization, and the method comprises the following steps: calculating the energy consumption intensity indexes of all devices in different time periods, and forming a time segment set of historical energy consumption abnormity of the devices; generating a candidate scheduling strategy, for any strategy, obtaining an equipment calling sequence and energy consumption switching time between equipment, and when the scheduling time of the equipment falls into a historical energy consumption abnormal paragraph set, calculating the actual operation energy consumption of the equipment in an abnormal time period; traversing the equipment calling time in the strategy to calculate the actual total energy consumption and the number of times exceeding the predicted energy consumption, calculating the energy efficiency matching value of each candidate strategy, and generating a strategy recommendation sequence; and selecting the strategy with the highest energy efficiency matching value from the strategy recommendation sequence as a preferred execution scheme, collecting real-time energy consumption data after execution, calculating an energy consumption deviation rate, comparing the energy consumption deviation rate with a threshold value, and judging the effectiveness of the strategy, so that energy consumption prediction and optimization are realized.

The invention relates to a transformer fault monitoring method and system based on artificial intelligence, and relates to the field of transformers, and the method comprises the steps: obtaining current data, temperature data, infrared video and load data of a transformer in a preset historical time period, and an audio oscillogram during operation; and searching a reference audio oscillogram from a preset database based on the current data, the temperature data and the load data, comparing the reference audio oscillogram with the audio oscillogram to determine an inconsistent waveform segment in the audio oscillogram, and determining the operation stability of the transformer based on the inconsistent waveform segment and the infrared video. And determining a potential fault area of the transformer according to the operation stability and the infrared video. The method has the effect of finding the accurate position of the potential fault of the transformer more timely.

The invention discloses an early warning method and device for multi-device time sequence data and electronic equipment. The method comprises the steps of determining a time sequence data stream; dividing the time sequence data stream according to the model identifier to obtain a plurality of sub-time sequence data streams, sliding on the sub-time sequence data streams by using the time windows according to a preset step length from the starting timestamps of the sub-time sequence data streams, and determining the time sequence data under the time window which slides each time as a target time sequence data stream; performing timestamp alignment and data filling on time sequence data in the target time sequence data stream to obtain an aligned target time sequence data stream, and predicting a time sequence data predicted value corresponding to the aligned target time sequence data stream in a future time period through a prediction model corresponding to the aligned target time sequence data stream; and determining early warning information corresponding to the target time sequence data stream according to the time sequence data predicted value. The technical problem that the accuracy of an early warning result is affected by data time sequence dislocation caused by inconsistent acquisition time of time sequence data between devices is solved.

The invention belongs to the technical field of internet online conference scheduling, and particularly relates to an internet-based online conference scheduling system and method. According to the method, a space-time weight mechanism ensures that a high-priority scheme is generated in one step, multiple rounds of back-and-forth communication are reduced, overlapping and absent risks in a selected time period are effectively avoided through historical behavior prediction and conflict verification, an interactive feedback mechanism quickly responds to newly-added conflicts, the stability of the scheme is improved, an audio and video transmission strategy is dynamically reserved, and the user experience is improved. Resource allocation and historical QoS data driving are intelligently adjusted according to real-time network conditions, delay, lagging and interruption caused by network problems in a conference can be remarkably reduced, in combination with personalized prediction of a topological structure, an internal cooperation habit, a visual time selection topological graph and an online negotiation tool are better matched and organized, transparency and sense of participation are enhanced, and the network quality is improved. And the multi-dimensional conflict and the space-time weight are fused, so that the balance optimization of the comprehensive cost and the efficiency is realized.

The invention relates to the field of band sawing machine control, in particular to a self-feeding horizontal band sawing machine control system. The system firstly collects a vibration signal and a torque signal of the band sawing machine in a preset time period before a current moment, and decomposes a part of the vibration signal in a preset time domain of a target moment; and according to the frequency bandwidth and the overall strength of each IMF signal at the target moment, the distribution of the center frequency of the IMF signal in the preset time domain at the target moment on the time sequence, and the difference of decomposition residuals between the target moment and each moment in the preset time domain, obtaining an interference index at the target moment, and according to the distribution and change of the interference indexes at all moments in the preset time period and the correlation between the interference indexes and the torque signals in the time sequence in the preset time period, the saw cutting speed adjusting weight at the current moment is obtained, and the saw cutting speed of the band sawing machine is adjusted in real time based on the saw cutting speed adjusting weight. The effect of controlling the horizontal band sawing machine can be improved.

A computerized-method for predicting a staffing-shrinkage percentage in a time-slot during a future staffing plan in a contact center, The computerized-method includes: (i) receiving via a User Interface that is associated to a Workforce Management (WFM) application, skills for the future staffing plan and the time-slot; (ii) calculating a historic-shrinkage percentage during a preconfigured period based on agents time-offs and understaffing-levels during the preconfigured period; (iii) determining the staffing-shrinkage percentage in the time-slot during the future staffing plan based on the calculated historic-shrinkage percentage; and (iv) configuring the WFM application to update staffing level in the future staffing plan, based on the determined staffing-shrinkage percentage.

The computer implemented methods, systems, and program instructions detect anomalies in a core network of a telecommunications network. The method comprises: receiving data representative of streams of time series data of a plurality of Key Performance Indicators (KPIs) of the performance of nodes of the core network; comparing the received time series data for each of the KPIs to predicted time series values for each KPI generated by one or more time series analysis algorithms trained with historical data for each KPI to predict the KPI over time; determining any KPIs having deviations between the received time series data and the predicted time series data during a specific time period, wherein each deviation is an anomaly; grouping the streams of time series data for each KPI determined to be deviated to generate anomaly data; using an artificially intelligent clustering algorithm to generate a plurality of clusters, wherein each cluster comprises a subset of the KPIs determined to be deviated that have been assigned to said cluster by the artificially intelligent clustering algorithm; wherein each of the clusters is identified as having an associated root cause.

The invention provides a rail transit equipment fault prediction method and system, and the method comprises the steps: obtaining the operation data and environment data of rail transit equipment in a current time period, and determining the time sequence correlation coefficient and statistical feature dispersion of the operation data, and an environment impact factor of the environment data affecting the fault of the rail transit equipment; determining a weight parameter of a time sequence prediction model according to the time sequence correlation coefficient, the statistical feature dispersion and the environmental impact factor, and determining a weight parameter of a classification model according to the weight parameter of the time sequence prediction model; inputting the operation data of the current time period into the time sequence prediction model, and obtaining the operation data of the rail transit equipment in the future time period according to the weight parameters of the time sequence prediction model; and inputting the operation data of the future time period into the classification model, obtaining the fault probability of the rail transit equipment according to the weight parameters of the classification model, and determining whether the rail transit equipment fails or not according to the fault probability. According to the invention, the accuracy and reliability of fault prediction are improved.

The invention relates to the technical field of data analysis, in particular to a terminal data secure transmission method and system for a cloud computer. The method comprises the steps of updating a relation graph between files according to operation conditions of the files in a current time period every target duration, and obtaining a current relation graph; the current time period is a time period corresponding to the recently elapsed target duration; respectively taking each file as a target file, and determining the current uploading bias of the target file according to the operation frequency of the target file in the current time period, the association condition performance of the target file in the current relation graph and the association condition change of the associated file of the target file; the associated file is a file associated with the target file in the relation graph in the current time period; and determining whether to upload the target file to the cloud computer or not according to the current uploading weight. Whether each file is uploaded to the cloud computer or not is accurately determined, and accurate and timely file uploading is realized.

The invention relates to the technical field of data processing, in particular to an automatic equipment data acquisition method and system, and the method comprises the steps: obtaining the voltage data and temperature data of each piece of equipment; dividing the voltage data and the temperature data of each device into a plurality of time windows; acquiring a change trend coefficient of each device in each time window, and acquiring a temperature influence coefficient of each device in each time window; acquiring a voltage transient coefficient of each device in each time window; fractal dimensions of all the devices in all the time windows are obtained; acquiring an electromagnetic disturbance coefficient of each device in each time window; acquiring a data acquisition disturbance value of each device in each time window; and the disturbance coupling index of each device in each time window is obtained, and then the filtering window parameter of each device in each time window is obtained, so that adaptive filtering of the voltage data is realized. By analyzing the environmental interference degrees of different devices in different time periods, adaptive filtering is performed on the acquired data, so that the accuracy of data acquisition is improved.

A sound diagnostic system includes an image forming apparatus and an information processing apparatus. The image forming apparatus includes operating portions, a sound collector for collecting a sonic wave, an acquiring portion, in each of a plurality of time sections when the sonic wave is collected by the sound collector, for acquiring a data including sonic wave level and an operating state of the operating portions. The information processing apparatus includes a generator for generating a threshold by adding a predetermined value to a first sonic wave level based on the sonic wave in a first period. The information processing apparatus determines a cause of an abnormal sound by comparing a second sonic wave level based on the sonic wave in a second period after the first period and the threshold. The predetermined value is set so as to be smaller as the first sonic wave level is larger.

The invention provides a storage performance monitoring method, electronic equipment, a readable storage medium and a program product, which can be applied to the technical field of information. The method comprises the following steps: acquiring processing time for completing an input / output operation when an operating system performs the input / output operation on a storage device; under the condition that the processing time of the input / output operation is longer than a first processing duration, storing operation related information of the input / output operation in an abnormal log of an abnormal log pool; obtaining a segmented time delay statistical table of the input / output operation in the first time period based on the processing time of the input / output operation; associatively storing equipment related information of the storage equipment executing the input / output operation in the first time period and the segmented time delay statistical table in a target file; according to the multiple processing times and the trigger judgment condition in the first time period, the dump file is transferred to the target address, and the dump file comprises at least one of the abnormal logs in the abnormal log pool and the target file.

The invention discloses a virtual digital human multimedia teaching interaction method and system and a storage medium, and the method comprises the steps: calculating an audio mouth shape time difference, a mouth shape subtitle time difference, an audio subtitle time difference and a video mouth shape time difference based on a same-window data group on the basis of a unified time aperture and a window, comparing the time differences with a time difference threshold value, and outputting a dislocation alarm result, unified constraint on four types of key alignment relationships is realized; when an alarm is triggered, selecting a degradation strategy according to a dislocation alarm result and generating an execution record, and summarizing the dislocation alarm result and the execution record to form an evidence chain data packet; when the cumulative number of times in the preset window number exceeds a diffusion blocking threshold value, recording output is switched into a video stream which retains audio and subtitles and does not overlap mouth shapes; and generating a correction release stream based on the evidence chain data packet, and replacing the recorded broadcast corresponding time slice and retaining the Hash verification value when the time sequence consistency index meets the release threshold, thereby realizing drivable treatment action, suppressible diffusion and replaceable correction.

A computerized process, a system, and non-transitory computer-readable medium having computer-executable instructions for mixing audio tracks according to a template. The process may include receiving at least one request for each of a plurality of time blocks of a template; querying a catalog of songs and / or song portions in a database to compile a candidate list of songs and / or song portions that substantially meet the at least one request of a first time block of the plurality of time blocks; choosing a first song portion and a second song portion from the candidate list for the first time block; compiling the first song portion and the second song portion to form at least a portion of the first time block, including blending a temporal length of the first song portion and the second song portion; and generating an audio file with the plurality of time blocks.

The invention relates to the technical field of edge computing, and particularly discloses a cloud edge collaborative management method and system based on edge computing, and the method comprises the following steps: marking data generated by the same project as target data, and taking an edge node used for storing the target data as a target node; dividing a time period, and determining a receiving node of the target data at the starting point of the time period i; target data generated in the time period i are transmitted to all receiving nodes, sub-time periods are divided, and integrity scores corresponding to the sub-time periods are determined; and determining a standard node according to the integrity score, and storing the target data in the time period i according to the standard node. According to the invention, the cloud edge collaborative storage and processing efficiency is improved.

The invention discloses a base station energy saving method and device, electronic equipment and a storage medium, and relates to the technical field of base station energy saving control. Conflict judgment is performed on different operation scenes of the train by means of a dynamic time warping algorithm so as to adjust an energy-saving scheme, and then the adjusted scheme is executed according to road sections, so that the dynamic change of the operation state of the train can be fully considered; the method can solve the problem that in an existing base station energy-saving method, due to the fact that a fixed time period or a strategy based on historical data prediction is adopted, dynamic changes of the train running state are not fully considered, base station resource configuration is not matched with the actual access requirement of a user, and resource waste or communication interruption is caused. The technical effects of realizing accurate energy saving of the railway communication network, improving the resource utilization rate and guaranteeing the communication stability are achieved.