4376 results about "Current time" patented technology

The invention discloses an index alarm method, system and device based on a dynamic baseline and a medium, and the method comprises the steps: collecting software and hardware operation index data, and carrying out the periodic statistics at a preset time interval; wherein the index data comprises real-time data and historical data; preprocessing the historical data; dynamically generating a baseline range based on the preprocessed historical data, and adaptively adjusting along with the periodic fluctuation of the historical data to reflect the index change trend of different time periods; the index data monitored in real time are compared with the dynamic baseline range of the current time period, the alarm boundary is calculated in combination with a preset tolerance threshold value, and the tolerance threshold value is the percentage offset of the baseline value; when the real-time data exceed the alarm boundary, triggering an alarm and generating notification information, and sending the notification information to the operation and maintenance terminal through a preset communication mode; and continuously optimizing the dynamic baseline according to newly added historical data, updating baseline parameters through a machine learning algorithm, and realizing self-adaptive iterative adjustment of an alarm threshold.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

The present disclosure generally relates to real-time communication user interfaces. A computer system displays a user interface corresponding to a real-time communication application that includes concurrently displaying a first user interface object corresponding to a first real-time communication session between a user of the computer system, where the first real-time communication session occurred prior to a current time, and a second user and a second user interface object corresponding to a second real-time communication session between the user of the computer system and a third user, where the second real-time communication session is scheduled to occur at a time after the current time. In response to receiving user input corresponding to a respective user interface object of the user interface, the computer system initiates a real-time communication session.

The invention relates to an electric power internet of things operation and maintenance management method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining sensor data, carrying out the cleaning and time alignment of the sensor data, and obtaining standard operation data; based on a historical data window corresponding to the standard operation data, calculating a mean value and a standard deviation of the historical data, and further generating a dynamic detection threshold value; comparing the standard operation data of the current time node with a dynamic detection threshold value, and screening out potential abnormal data; calculating an abnormal trend factor corresponding to the potential abnormal data, and determining an abnormal development trend of the potential abnormal data according to the abnormal trend factor; and determining a corresponding causal influence weight based on a preset causal relationship network, and calculating a fault score according to the causal influence weight and the abnormal trend factor, so as to generate an early warning report according to the fault score. The method has the effect of improving the management efficiency of the power equipment.

The invention provides a sensitive information detection method and content display method in a question and answer scene, and the method comprises the steps: obtaining the current output information of a large language model, and storing the output information to a preset buffer area; wherein the output form of the output information is streaming output; taking the output information and original information stored in a preset buffer area as a current to-be-processed text; in response to the fact that the data length of the current to-be-processed text exceeds a preset length, taking the current to-be-processed text as a target detection text; wherein the preset length is determined according to a pre-constructed sensitive word library; and performing sensitive word detection on the target detection text to obtain the detection result, thereby improving the detection speed of the sensitive information, reducing the waiting time of the user and improving the user experience.

The invention discloses a dynamic tension control method for a dry-method winding and unwinding system, solves the problems of high overshoot, long coil diameter sudden change adjustment time and large steady-state fluctuation in low-speed operation of the dry-method winding and unwinding system under a high-speed reversing working condition, and belongs to the technical field of composite material equipment development. The method comprises the steps of collecting data of a winding and unwinding system, identifying total rotational inertia and friction torque on line according to the data, obtaining a friction compensation item according to the friction torque, and obtaining a rotational inertia feed-forward item according to the total rotational inertia; the method comprises the following steps of S1, inputting the tension error and the tension error change rate of the winding and unwinding system into a fuzzy PID controller to obtain a PID parameter real-time adjustment amount, updating a PID parameter, and then obtaining a PID feedback control amount at the current moment k by using the PID controller and the tension error, and S3, synthesizing a friction force compensation item, a rotational inertia feed-forward item and the PID feedback control amount into a final control amount of the winding and unwinding system.

The invention relates to the technical field of circuit monitoring, in particular to an intelligent sensing and identifying method for circuit faults of a semiconductor hybrid solid-state circuit breaker. Obtaining a fluctuation abnormal moment according to the amplitude characteristic and the continuous characteristic of the current fluctuation in the current time sequence curve; obtaining a suspected current anomaly moment according to the difference characteristic of the fluctuation anomaly degree of the fluctuation anomaly moment and the same historical moment; clustering is carried out according to the current characteristics and fluctuation characteristics of all suspected current abnormal moments in the current time sequence curve, and different clusters are obtained; obtaining the current anomaly degree of the clustering cluster according to the quantity characteristics of the suspected current anomaly moments in the clustering cluster and the distance characteristics in the clustering space; obtaining a current abnormity moment according to the current abnormity degree; a fault characteristic value is obtained according to the distribution characteristic of the current abnormity moment and the change characteristic of the current abnormity degree in the current time sequence curve, and circuit fault identification is carried out, so that the working stability of the circuit breaker is improved.

The invention discloses an electric connector fault prediction method, and the method comprises the steps: synchronously obtaining multiple types of time series data of a contact area of a target electric connector in a preset time window based on a preset data collection device, and the time series data comprise temperature spatial and temporal distribution time series data, contact resistance transient time series data and a surface image sequence; performing feature analysis on the multi-source time sequence data by using a preset time sequence coupling sign extraction mechanism to obtain a multi-source phase change feature vector of the target electric connector in the current time window; inputting the multi-source phase change feature vector into a pre-trained electric connector health state prediction model, and outputting a prediction result of the health state of the target connector at the current moment, including a health state index; and performing differential early warning based on the predicted health state index of the target connector. Therefore, early and quantitative accurate prediction of the state of the electric connector is realized.

Systems and methods for detecting intermittent connections in a battery energy storage system (BESS) subsystem are disclosed. For each battery rack in the BESS subsystem, current time series data is received. A current rolling average is determined for each data point of the time series data by averaging current values of timestamps over a specified time interval. A current delta is determined for each data point, where the current delta is a difference between the current rolling average associated with the battery rack under analysis and a mean of the current rolling averages associated with the other battery racks in the BESS subsystem. A distribution profile of the current deltas is analyzed to detect a current anomaly at the respective battery rack. In response to detecting the current anomaly at the battery rack under analysis, an alert indicative of a faulty connection is displayed on a user interface.

The invention relates to the technical field of data processing, in particular to an equipment state monitoring method and system based on data analysis. The method comprises the steps that multiple real-time monitoring parameter sequences, corresponding to different monitoring parameters, of the packaging equipment in the current time period are obtained; determining a cross-correlation sequence between a historical monitoring parameter sequence corresponding to the target monitoring parameter and the real-time monitoring parameter sequence so as to determine a volatility parameter value of an extreme point in the cross-correlation sequence; determining a difference degree value corresponding to the real-time monitoring parameter sequence; taking the product of the volatility parameter value corresponding to the target monitoring parameter and the difference degree value as the weight value of the target monitoring parameter; and constructing an isolated forest according to the weight values of the different monitoring parameters, and obtaining an abnormal score value of the current time period by using the isolated forest so as to monitor the equipment state of the packaging equipment. According to the technical scheme, the state of the packaging equipment can be better monitored.

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 present disclosure generally describe user interfaces related to time. In accordance with embodiments, user interfaces for displaying and enabling an adjustment of a displayed time zone are described. In accordance with embodiments, user interfaces for initiating a measurement of time are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface using a character are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface that includes an indication of a current time are described. In accordance with embodiments, user interfaces for enabling configuration of a background for a user interface are described. In accordance with embodiments, user interfaces for enabling configuration of displayed applications on a user interface are described.

The invention relates to the technical field of energy storage load scheduling, in particular to an energy storage load scheduling method and system based on multi-objective optimization, and the method comprises the steps: obtaining historical electricity price, load power and renewable energy power generation power data of each time period before the current time as input variables, and carrying out the normalization processing to obtain input data; inputting the input data into an LSTM-Transformer prediction model, and predicting to obtain the power grid electricity price, the load demand and the renewable energy power generation amount in a future time period; establishing a multi-objective optimization model including electric charge cost, operating profit and energy storage life; substituting the prediction data into the multi-objective optimization model, and solving a Pareto frontier based on a multi-objective optimization algorithm to obtain a non-inferior solution set satisfying the multi-objective optimization model; and taking each solution in the non-inferior solution set as input, and screening the solution with the maximum Sharpley value as an optimal energy storage charging and discharging adjustment strategy. By accurately controlling the working state of the energy storage system, the interactive operation mode of the energy storage system and the power grid is optimized.

The present application relates to a waveform file conversion method and apparatus for chip testing, and a computer readable storage medium. The method comprises: performing content analysis on a waveform file to be converted, to acquire period information, pin information, and state information of pins in each period of said waveform file; generating a PAT-format digital signal waveform file; and on the basis of a mapping relationship between pins and ATE channels, converting the PAT-format digital signal waveform file into a binary vector file for ATE testing. A waveform file in any format is converted into a PAT-format digital signal waveform file which uses periods as rows and pins as columns, so that each row vector not only contains current time information but also contains a logical state of each pin; in addition, to facilitate understanding, the PAT-format digital signal waveform file is further converted into a binary vector for ATE testing, so that efficient processing of waveform files in a plurality of different formats can be realized, thereby improving the universality of conversion.

The invention discloses a power system intelligent monitoring method based on artificial intelligence, and the method comprises the steps: carrying out the real-time prediction and analysis of newly-added data through an adjusted model parameter set in combination with the core knowledge representation of historical data, obtaining an operation state evaluation value of a current time period, and judging whether a system has an abnormal fluctuation trend or not; after the abnormal fluctuation trend is obtained, extracting a key mode from the existing knowledge by utilizing a pre-established knowledge distillation framework for the part of the evaluation value exceeding the normal range, and obtaining a refined knowledge subset; according to the refined knowledge subset and the feature vector set of the newly added data, performing fine adjustment on the model, and determining an updated monitoring model; according to the method, repeated calculation of total data in a traditional method is avoided, the data processing efficiency is greatly improved, the time requirement of real-time monitoring is met, the problem that model updating is lagged in the traditional method is solved, and the problem that precision is difficult to maintain after newly-added data is introduced is solved.

The invention belongs to the technical field of advertisement putting, and discloses a multi-modal data driven intelligent advertisement putting system and method. Comprising the following steps: acquiring and analyzing historical behavior data of a user, extracting a periodic rule of user activity, and identifying candidate delivery windows; when it is detected that the current time is matched with the candidate delivery window, collecting real-time behavior data; performing feature extraction on the real-time behavior data to obtain situation features, and identifying key situation factors to form a user situation portrait; according to the situation portrait of the user, dynamically evaluating interruption cost and response probability, and judging whether the current time is a suitable delivery opportunity, and if so, performing advertisement delivery; according to the invention, comprehensive perception and analysis of user behaviors and situations are realized, and forced pushing under the situation that the user is not suitable for receiving the advertisement is avoided, so that the user experience is improved, and the accuracy and effect of advertisement putting are effectively improved.

The invention provides a controller parameter optimization method and a task control method based on reinforcement learning, and relates to the technical field of control, and the method comprises the steps: inputting the parameters of a PID controller of a current task into a DDPG model, obtaining the parameters of a PID controller of a next task, inputting the parameters of the PID controller of the next task into the PID controller to obtain a control quantity; determining an evaluation result of the control of the PID controller based on the control quantity as a reward; taking the state, action and reward of the current time step and the state of the next time step as sample data and storing the sample data into an experience pool; and training the DDPG model based on the experience pool, and determining the target parameter output by the trained DDPG model as the optimal parameter of the PID controller. In the application stage, only the PID controller needs to output the target control quantity based on the optimal parameters, the participation of a DDPG model is not needed, and the light weight of the whole control model is realized.

The invention discloses a charging parameter optimization method and system based on artificial intelligence, and relates to the field of unmanned aerial vehicle charging, and the method comprises the steps: obtaining the battery type, current electric quantity and remaining flight tasks of an unmanned aerial vehicle; determining a charging mode and an initial charging parameter of the unmanned aerial vehicle based on the battery type, the current electric quantity and the remaining flight tasks; in response to the charging signal of the unmanned aerial vehicle, monitoring in real time to obtain a battery charging curve of the unmanned aerial vehicle; based on the battery charging curve of the current time period, an artificial intelligence model is adopted, charging parameters of the unmanned aerial vehicle battery in the next preset time period are dynamically adjusted at the end moment of the current time period, and the charging parameters comprise charging current and charging voltage; and charging the battery of the unmanned aerial vehicle according to the charging parameters at the starting moment of the next preset time period. The charging time can be shortened to the greatest extent on the premise of ensuring the safety and the service life of the battery of the unmanned aerial vehicle.

The embodiment of the invention discloses a dust monitoring system and method for a photovoltaic module. The method comprises the following steps: firstly, carrying out data preprocessing on illumination intensity values at a plurality of preset time points and output current values of a monitored photovoltaic module at the plurality of preset time points to obtain an illumination intensity time sequence input vector and an output current time sequence input vector; performing time sequence analysis on the illumination intensity time sequence input vector to obtain a sequence of illumination intensity local time sequence feature vectors; and integrating output current time sequence information expressed by the output current time sequence input vector into a sequence of the illumination intensity local time sequence feature vector to obtain an illumination intensity time sequence embedded output current time sequence feature vector, and finally, based on the illumination intensity time sequence embedded output current time sequence feature vector, obtaining an illumination intensity time sequence embedded output current time sequence feature vector. And determining whether the dust coverage rate of the monitored photovoltaic module exceeds a predetermined threshold. In this way, whether cleaning is needed or not can be intelligently judged.

A building management system includes one or more computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive threats, the threats each indicating an incident affecting a dynamic risk score associated with an asset, wherein one or more of the threats are current threats that are active at a current point in time and one or more of the threats are historic threats that were active at one or more past times. The instructions cause the one or more processors to generate, based on the one or more current threats, the dynamic risk score at the current point in time, generate, based on the one or more historic threats, a baseline risk score, and cause a user interface to display an indication of the dynamic risk score at the current point in time and an indication of the baseline risk score.

The invention relates to a scraper chain tension dynamic optimization method based on real-time load feedback, and belongs to the technical field of scraper chain control. The method comprises the following steps: acquiring load data of a scraper chain in various operation states; carrying out standardization processing on the load data of various operation states and then extracting features to obtain load time sequence features; carrying out time attention mechanism processing on the load time sequence characteristics and the load data of the multiple operation states to obtain importance weights; a tension value is calculated according to the load data of each operation state and the importance weight of the load data; calculating a real-time working condition coefficient of the current time node, and dynamically adjusting a preset safety tension range to obtain a real-time safety tension range of the current time node; comparing the tension value with a real-time safe tension range, and generating a control instruction according to a comparison result; and executing the control instruction. The tension control method of the scraper chain is optimized, and the control precision of the tension of the scraper chain is improved.

The invention discloses a token synchronization method and device based on micro-service, equipment and a storage medium, and relates to the technical field of application software development, and the method comprises the steps: analyzing a target request comprising a first token to obtain token expiration time; and if the current time does not exceed the token expiration time, judging whether the remaining effective duration is greater than a target duration threshold, if the remaining effective duration is not greater than the target duration threshold and a token refreshing mark does not exist, setting the token refreshing mark in the preset cache database, refreshing the token, and issuing an obtained second token to the front-end micro-service, the front-end micro-service accesses the back-end micro-service based on the second token; and calling the independent micro-service to analyze a first token issued by the front-end micro-service to obtain the user identifier, so that the independent micro-service accesses a preset cache database by using a key generated based on the user identifier to obtain a second token, and accesses the rear-end micro-service based on the second token, and enabling the back-end micro-service to transmit the access response information to the independent micro-service for processing. Therefore, the token synchronization efficiency can be improved.

The invention discloses an intelligent detection system for a range hood, and the system employs a pressure difference sensor and a motor current sensor to collect the front and rear pressure differences of an air duct and the working current time sequence of a motor, and carries out the feature-level deep fusion of the two. Oil stain state multi-sensor time sequence fusion representation capable of comprehensively reflecting multi-dimensional information such as air duct blockage and motor load is obtained; meanwhile, in order to ensure the reliability of a final decision result, a plurality of independent oil stain indexes are obtained through different input characteristics, confidence coefficient verification is carried out on a fusion model output result based on fluctuation values among the indexes, and then the oil stain state of the range hood is determined based on a verification result. Through the mode, the risk caused by abnormal samples or accidental misjudgment is remarkably reduced, the overall stability and credibility of the system are improved, and a solid foundation is provided for efficient, safe and intelligent kitchen environment management.

The invention relates to the technical field of energy storage, and discloses a power load prediction method and system for an energy storage system, and the method comprises the steps: obtaining a load sequence and a meteorological sequence of a current time period; judging whether a sequence of which the similarity with the load sequence exceeds a preset threshold exists in the historical load sequence of the energy storage system or not; if not, according to the load sequence and the meteorological sequence, obtaining an initial load prediction sequence of a future time period after the current time period; correcting the initial load prediction sequence to obtain a first load prediction sequence; if yes, obtaining a first load sequence with the highest similarity, and obtaining an actual load sequence and an actual weather sequence of a target time period after the first load sequence; obtaining a predicted meteorological sequence corresponding to a future time period; calculating a meteorological difference sequence between the actual meteorological sequence and the predicted meteorological sequence; and obtaining a load difference sequence according to the meteorological difference sequence, and superposing the load difference sequence to the actual load sequence to obtain a second load prediction sequence.

The invention relates to the technical field of baby diapers, in particular to an intelligent baby diaper replacement reminding system integrated with hydrogen sulfide detection, which is used for determining a reference acquisition frequency of each time interval in a time period based on historical multi-dimensional monitoring data of a target user; then determining the necessity of hydrogen sulfide detection at the current acquisition moment based on the multi-dimensional monitoring data in the backtracking time window at the current acquisition moment, and then adjusting the reference acquisition frequency of the current time interval based on the necessity of hydrogen sulfide detection at the current acquisition moment to obtain a final acquisition frequency; acquiring multi-dimensional monitoring data based on the final acquisition frequency in the remaining time interval in the current time interval; and finally, based on the multi-dimensional monitoring data acquired in real time, generating a diaper replacement reminding signal.

The invention discloses a quick data traceability method and system based on multi-source log analysis, and relates to a data processing system or method specially suitable for administrative, commercial, financial, management, supervision or prediction.The method comprises the steps that firstly, the system responds to a service instruction to generate a transaction receipt containing a unique serial number and service attribute parameters; and establishing an association relationship between the two records and generating a to-be-confirmed record and a timestamp. And sending a service message containing the service attribute parameters to a receiving end. When an ownership right reverse search request of a receiving end is received, the system calculates an effective time interval based on current time and a preset time delay threshold value, and screens out a target to-be-confirmed record which is matched with a service attribute parameter and generates a timestamp in the interval. And finally, extracting the unique serial number from the target record, generating a confirmation receipt and sending the confirmation receipt to a receiving end system. The method is used for reducing the calculation overhead and false alarm rate of accurately positioning specific abnormal transactions in massive logs by a system, and improving the confidence and timeliness of an automatic traceability result.

A time series data prediction method and apparatus, and a storage medium are provided. The method includes: obtaining current time series data collected in a current time window that is adjacent to and precedes a prediction time window in a current time period, and obtaining a plurality of groups of historical time series data separately collected in a same target time window of a plurality of historical time periods; encoding the plurality of groups of historical time series data by using a plurality of encoders respectively, to obtain a plurality of historical time series features, where each historical time series feature represents relative location information and change trend information of each group of historical time series data in the target time window; and determining, predicted time series data corresponding to a target object in the prediction time window.

The invention provides a digital clock remote metering method based on network delay suppression, which comprises the following steps of: constructing a layered synchronous architecture, deploying a main clock source, a plurality of regional clock servers and a plurality of terminal devices, connecting the plurality of regional clock servers with the main clock source through optical fiber special lines to obtain reference time, each regional clock server is connected with a plurality of terminal devices through a 5G / industrial Ethernet to form a three-level synchronous network; rTT data of the terminal device and a regional server are obtained through bidirectional timestamp interaction, and message screening is carried out according to a preset rule; calculating a final time delay compensation value by adopting a mixed time delay compensation algorithm, and calibrating a terminal clock by adopting the final time delay compensation value; and adopting a time selection algorithm to obtain an optimal reference source for data synchronous analysis. According to the invention, the time deviation problem between the current time indicating value of the digital clock and the standard time and the data synchronization analysis problem can be solved.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention discloses an aircraft deployment method based on a dynamic airspace network and reinforcement learning, and relates to the field of air traffic management, and the method comprises the steps: obtaining flight information and task priority weight information of each flight in a historical time period and a current time period; dynamically dividing the airspace into an independent sub-airspace and a shared airspace by using flight information in a historical time period; calculating the safe area of each flight through a space-time buffer zone model, and determining a flight path according to the safe area; and according to the flight information, the airspace division result and the flight path, a release action instruction set including height adjustment, course correction and speed change is generated, and real-time deployment is performed on the flight. The technical effects of the invention lie in that airspace changes can be responded in real time, flight paths of flights are optimized, potential conflicts are reduced, and the airspace utilization rate is improved, so that the air traffic control efficiency and the flight safety are improved.