1669 results about "Time segment" patented technology

The invention discloses an energy management system data filling method and architecture based on time sequence analysis, and the method achieves the intelligent prediction and filling of a data missing segment in an energy management system through constructing a filling model based on a naive Bayes algorithm, and improves the data integrity and continuity. The system firstly dynamically manages historical data and new data through a sliding window mechanism to ensure that the model can fully consider the change trend of time sequence data in the filling process. The filling model combines the distribution characteristics of historical data and current data during initialization, and endows the data in different time periods with weights, so that the model can adapt to the equipment state and environment change. And after the data missing segment is detected, the system immediately calls the filling model to carry out prediction filling, replaces the original missing data with the result and writes the result into the database, so that the accuracy of subsequent analysis and anomaly detection is ensured.

A method performed by a node for handling drift in data. The node obtains a dataset including a plurality of datapoints corresponding to a plurality of values of one or more dependent variables for a plurality of first features over a time period. The node determines, using machine learning and explainability, in the absence of determining whether or not the plurality of datapoints has a drift, whether or not there has been a change in respective one or more characteristics of a subset of the plurality of first features having a largest contribution to a variability of the datapoints in the plurality of datapoints based on a threshold from a first time period to a second time period. The node then initiates application of a drift policy on the plurality of datapoints based on a result of the determination.

Systems and methods are described for detecting anomalies within time series data using one or more online machine learning models. In one example, time series data may be obtained that spans a moving time period of a set length, to be used as training data. A machine learning model may be trained using the training data to identify anomalies in the time series data by upon detecting an estimation of an anomalous value in the time series data, pausing input of additional values of the training data into the machine learning model, and resuming input of the additional values of the training data into the machine learning model based on detecting at least one non-anomalous value. The trained machine learning model may then be used to detect a first anomaly in additional time series data by comparing the at least one additional value to the trained machine learning model.

It aims to appropriately manage the bias of the signal processing amount. 【Solution means】 The abnormality management device 1 uses, as teacher data, normal data indicating the number of normal packets distributed to each device for each time zone, and learns, by maximum likelihood estimation, the parameters of a probability model that outputs the posterior probability that the number of packets distributed to each device for each time zone is normal. A learning unit 11, the posterior probability for each device for each time zone estimated by the learned probability model, the probability distribution of the normal data for each device estimated based on the normal data, and the prior probability of being normal. A derivation unit 12 that derives the probability distribution of abnormal data indicating the number of abnormal packets distributed to each device in the time zone, and the probability distribution of the normal data and the probability distribution of the abnormal data for each device for each time zone are equal. And a setting unit 13 that sets, as a threshold value for abnormality determination of the number of packets distributed to each device, the number of packets corresponding to the value of the probability distribution that becomes the value.

Decoding method and memory system which group bits in irregular LDPC codes having similar degrees of convergence into respective degree groups, classify the degree groups according to a metric indicative of a number of decoding iterations for convergence, divide a time period for convergence of the decoding iterations into different zones for the processing of selected degree groups within each zone, and skip decoding of the bits in a non-converging zone where the bits are not converging.

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.

To provide a power amount adjustment system capable of reducing power consumption according to requests to reduce the power consumption due to tight power supply and demand.SOLUTION: In a power amount adjustment system 100, when a server 4 acquires tight power supply and demand information requiring a reduction in power consumption in a specific time period, the server is configured to change and adjust a start time of defrosting operation when the specific time period overlaps with a time period during which pulldown is scheduled to be performed.SELECTED DRAWING: Figure 1

The invention discloses a power consumption distribution method and device, a storage medium and a program product, and relates to the technical field of servers, and the method comprises the steps: dividing a plurality of time slots, and in each time slot interval, according to the predicted load demand of each node and the task priority weight of a task executed by each node in the corresponding time slot, determining the task priority of each node; according to the method, the dynamic priority of each node is determined, and then the power consumption of each node is allocated based on the dynamic priority and the resource budget of each node in each time slot interval, so that the method can flexibly adapt to different load requirements and execution of tasks with different priorities in different time periods, and the tasks with high priorities can guarantee more resources; according to the method, resource waste is avoided, reliable decision support is provided for power consumption management of the server, system stability and task execution efficiency are effectively balanced, and double breakthrough of the data center in the aspects of energy conservation and performance optimization is promoted.

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 a circuit breaker mechanical fault identification method and device. The method comprises the following steps: acquiring SOE data and sound signal data of a circuit breaker in a preset time period; time features and action features are extracted from the SOE data, and a plurality of stages of the circuit breaker in the preset time period are identified according to the action features; extracting domain features corresponding to the stages from the sound signal data according to the time features; and inputting the domain feature corresponding to each stage into a pre-trained corresponding prediction model to obtain a fault identification result. According to the application, the original structural characteristics of the circuit breaker can be prevented from being changed in the fault identification process, and the accuracy of fault identification is improved at the same time.

Aspects of this technical solution can include obtaining, by one or more processors coupled with non-transitory memory, a first plurality of images each including corresponding first time stamps, the plurality of images corresponding to video data of a physical environment, extracting, by the one or more processors and from among the first plurality of images, a second plurality of images each having corresponding second time stamps and each having corresponding features indicating an object in the physical environment, and training, by the one or more processors and with input including the features and the second time stamps corresponding to the features, a machine learning model to generate an output indicating a pattern of movement of one or more objects corresponding to the features and the second time stamps corresponding to the features.

Devices, networks, systems, methods, and processes for utilizing a time-based queue are described herein. A device may receive an object stream comprising multiple objects. The device may assign a Time-To-Live (TTL) value for each object in the object stream and a maximum TTL value for the object stream. The device may create and store the time-based queue in a memory based on the maximum TTL value. The device may insert the objects in the time-based queue at corresponding object insertion times. The device can pop the objects out of the time-based queue in First-In-First-Out (FIFO) order. The objects may expire when time periods indicated by corresponding TTL values elapse after the corresponding object insertion times. The device can further remove the expired objects from the time-based queue. The time-based queue stored in the memory can include references to the objects stored in a time-based storage in form of an array.

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 invention discloses a real data generation method and system, a storage medium and a program product, and the method comprises the steps: carrying out the trend analysis of target data, and obtaining a trend coefficient of the change trend of the target data; constructing a global trend function according to the trend coefficient; obtaining a primary period value and a secondary period value of the target data; constructing a period superposition function according to the primary period value and the secondary period value; performing function synthesis on the global trend function and the periodic superposition function to obtain a time sequence characteristic function; acting the time sequence characteristic function on each time sampling point of the target time period to obtain a reference data sequence; performing random disturbance processing on the reference data sequence according to preset fluctuation parameters to obtain a target data sequence containing random fluctuation characteristics; and adjusting the target data sequence according to a preset data constraint condition, and generating real data meeting the data constraint condition. According to the method and the device, the accuracy of reflecting the time sequence evolution rule by real data is improved, and the capability of discovering defects in time sequence related function tests is further improved.

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 techniques disclosed herein enable systems to generate intuitive security reports that illustrate security events that occur during a predefined time period (e.g., thirty days). The security report visual can include a pair of parallel axes where the first axis includes a plurality of time-series segments respectively representing security event types (e.g., obfuscation, phishing) and the second axis includes two time-series segments representing security events that are not remediated and security events that are remediated. A plurality of first lines originating at respective points along the first axis and terminating within a GUI element can represent security events of various types that occur at a particular time unit (e.g., a day) within the predefined time period. A plurality of second lines originating within the GUI element and terminating at respective points along the second axis can represent security events that are either remediated or not remediated.

The embodiment of the invention provides a fault positioning method and device, a storage medium and a program product, relates to the technical field of communication, and can realize quick fault positioning and improve the fault positioning efficiency. The method comprises the steps that alarm information sent by first equipment is received, the first equipment comprises a plurality of ports, a first flow rate time sequence and a second flow rate time sequence of the flow rate of each port in a first time period are determined, and the first flow rate time sequence and the second flow rate time sequence are sent to the first equipment; and finally, based on the first flow rate time sequence and the second flow rate time sequence of each port in the plurality of ports, determining a port in a fault state in the first equipment. Wherein the first flow rate time sequence is used for representing the change condition of the actual flow rate of the port in the first time period, and the second flow rate time sequence is used for representing the change condition of the predicted flow rate of the port in the first time period.

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.

A method for synchronizing an action of a target object with source audio is provided. Facial parameter conversion is performed on an audio parameter of the source audio at different time periods to obtain source parameter information of the source audio at the respective time periods. Parameter extraction is performed on a target video that includes the target object to obtain target parameter information of the target video. Image reconstruction is performed on the target object in the target video based on the source parameter information of the source audio and the target parameter information of the target video, to obtain a reconstructed image. Further, a synthetic video is generated based on the reconstructed image, the synthetic video including the target object, and the action of the target object being synchronized with the source audio.

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 discloses a train stop control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining each first deviation value of train stop in a historical time period, the first deviation value being a position deviation value between an actual stop point of a train at a platform and a preset target stop point; determining a deviation average value and a deviation standard deviation according to each first deviation value; according to the deviation average value, the deviation standard deviation and a normal distribution relational expression, a predicted deviation value of the train stopping at the next platform is determined; in the process of driving the train to the next platform, for each preset period, acquiring a first distance between the train and a target parking point of the next platform in the period; determining a second distance according to the first distance and the predicted deviation value; determining a first speed of the train in the period according to the second distance; and the train is controlled according to the first speed in the period, and the train station stopping accuracy is improved.

The invention relates to the technical field of PLC linkage control, in particular to a multi-effect evaporator set and a PLC linkage control method thereof.The method comprises the steps that data of all kinds of evaporation parameters in all time periods in the operation process of the multi-effect evaporator set are obtained, and all parameter data sequences are formed; obtaining the standard floating stability of the evaporation parameters in each time period, and obtaining the overshoot optimization quantity and oscillation effect adjacency of the evaporation parameters in each time period; optimizing a dynamic proportion parameter, a dynamic integral parameter and a dynamic differential parameter of each evaporation parameter in the next time period based on the dynamic proportion parameter, the dynamic integral parameter and the dynamic differential parameter; and performing PLC linkage control on the multi-effect evaporator group by adopting a PID algorithm through the optimized dynamic proportion parameter, dynamic integral parameter and dynamic differential parameter. The operation state of the multi-effect evaporator can be accurately controlled.

The invention relates to the technical field of electric power fault detection, and discloses a fault monitoring system and method based on an electric power acquisition terminal, and the method comprises the steps: constructing a non-linear relation between the power consumption and the temperature through analyzing the historical power consumption data of a user and the weather temperature; the method comprises the following steps: dividing a user electricity consumption day into a plurality of time periods, and setting a time interval threshold value; and in each time period, data are acquired through the electric power and temperature acquisition devices, an electric quantity and temperature scatter diagram is constructed, and nonlinear data fitting is carried out. Thirdly, the similarity between the temperature nonlinear relational expressions of different time periods is calculated, the time periods are divided into sets, and a temperature retrieval set is established; and searching a corresponding electric quantity relation set by matching the temperature nonlinear relational expression of the user in the current time period, and judging whether electric power fluctuation and fault exist or not according to the priority ranking and the relation similarity.

The embodiment of the invention discloses a power load prediction method and device, equipment, a medium and a program product, and the method comprises the steps: determining first time sequence feature data corresponding to a preset time period based on date features and weather features corresponding to each day in the to-be-predicted preset time period; on the basis of the first time sequence characteristic data, through a target load prediction model, a load prediction result corresponding to a preset time period is obtained through prediction; the target load prediction model is a time sequence converter model based on data blocks; the target load prediction model is obtained by training the initial load prediction model based on second time sequence characteristic data corresponding to a historical time period and corresponding load label data; and the second time sequence characteristic data is determined based on the date characteristic and the weather characteristic corresponding to each day in the historical time period, so that the adverse effects of data time delay, holidays, extreme weather and the like can be avoided or reduced, and the accuracy of a load prediction result is effectively improved.

The invention belongs to the technical field of equipment management and control, and provides a BIM-driven equipment full-life-cycle intelligent management and control method and system, and the method comprises the steps: carrying out the updating of a BIM model according to the data change of equipment in each time period in a full life cycle, and carrying out the lightweight processing of the BIM model; specifically, when lightweight processing is carried out, attribute information of a preset type in an equipment model is deleted by reducing the number of triangular meshes in a repeated structure and a pre-selected local structure in a BIM model, and the precision of the equipment model is dynamically adjusted according to different time periods of the equipment; according to the method, the repeatability and importance of the structure in the BIM model are considered, dynamic precision adjustment is performed according to equipment in different time periods, the problem of incomplete equipment data analysis caused by lightweight processing is avoided, the requirements on storage capacity, data transmission capacity and performance are reduced on the basis of ensuring data processing precision, and the data processing efficiency is improved. And the application of the BIM model in the full life cycle management and control of the equipment is promoted.

The embodiment of the invention provides a resource scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining task meta-information and task constraint information of each task in a task set, and determining to-be-executed tasks in each time period based on the task meta-information and the task constraint information, determining the total resource consumption amount of each time period based on the resource consumption amount of the to-be-executed tasks, determining a target time period in which the total resource consumption amount is greater than the set total available resource amount from each time period, and obtaining adjustable tasks from the to-be-executed tasks in the target time period, and determining the scheduling priority of each adjustable task based on the resource consumption of the adjustable tasks, the downstream task quantity and the task running duration, determining the adjustable task with the lowest scheduling priority as a target task, and adjusting the predicted starting time of the target task according to a preset adjustment strategy.

The invention discloses a multi-pedestrian tracking method and system based on short track splicing. The method comprises the following steps: firstly, detecting a video frame by using a YOLOX detector; secondly, performing detection and short-term tracking on the target in the video frame in each time period to generate a short track; then, the similarity between the tracks in the adjacent time periods is measured by calculating the appearance features and the motion features of the tracks, and the similar short tracks are spliced into a long track based on the similarity measurement; and finally, the spliced tracks are cleaned and smoothed, and the problem of track breakage caused by shielding is solved. According to the method, the robustness of multi-target tracking is effectively improved, and the method is particularly suitable for complex scenes with dense targets and serious shielding. A video sequence is segmented to generate a short-term target trajectory, and trajectory splicing is realized by using trajectory similarity measurement, so that a longer and more robust target trajectory is formed.