3884 results about "Current time" patented technology

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 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.

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 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 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 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 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 provides a resource scheduling processing method, device and system, a medium and a product. The method is applied to a cloud desktop management system, and comprises the following steps: according to obtained historical data of a cloud desktop instance running on each computing node in a to-be-scheduled cloud pool, calculating and obtaining a use probability of the cloud desktop instance on each computing node in a next time period of a current time period; for each computing node, according to the use probability of the computing node, predicting the increased or reduced resource overhead of the computing node in the next time period of the current time period, and according to the resource overhead, computing the resource load of the computing node in the next time period of the current time period; and determining a to-be-migrated computing node and a target computing node according to the resource load of each computing node and a pre-configured resource use threshold, obtaining a first target cloud desktop instance on the to-be-migrated computing node based on a pre-configured migration strategy, and migrating the first target cloud desktop instance to the target computing node. According to the method provided by the invention, the resource utilization rate of each computing node in the cloud pool is effectively improved, so that the normal use of the cloud desktop is ensured.

The invention discloses a communication method and system of a wireless ad hoc network, and relates to the technical field of wireless ad hoc networks. The method is applied to a data link layer of the wireless ad hoc network, the wireless ad hoc network comprises a plurality of nodes, each node communicates according to a time frame period, each node comprises an interception mode and a sending mode, and the method comprises the following steps: in the current time frame period, each node executes the following steps: receiving data in the interception mode, and sending the received data in the sending mode; updating a node neighbor table based on the received data; judging whether data sending time is up or not according to the information of each neighbor node in the node neighbor table; and switching to the sending mode to send the data under the condition of determining that the data sending time is up, and switching to the interception mode after the data is successfully sent. The whole wireless ad hoc network does not need complex interactive protocols such as time slot distribution, time slot recovery and the like, so that the decentralized wireless ad hoc network in a real sense is realized.

The invention provides a SaaS service security verification method and system under a zero-trust architecture. The method comprises the following steps: a client sends a login request to a server; the server side verifies the login request and returns a dynamic key, a coding rule table and a shuffling rule to the client side based on the login request and a current time window; the client generates a first password table corresponding to the dynamic key, hashing data of the service request according to a coding rule table, embedding the data into the first password table, performing shuffling processing of various different schemes on the first password table according to a shuffling rule to obtain a client request signature, and sending the service request and the client request signature to the server; and the server side obtains the server side request signature according to the same logic, verifies the client side request signature based on the server side request signature, and returns a response result corresponding to the service request to the client side after the verification is passed. According to the method, the end-to-end data integrity and security in the communication process of the client and the server can be remarkably improved.

The invention relates to the technical field of data processing, in particular to an electric energy meter fault detection method based on multi-sensor data fusion, and the method comprises the steps: judging whether an electric energy meter generates data drift abnormity or not in a current time period according to the data change trend of each monitoring index in the current time period; if the data drift abnormity of the electric energy meter occurs in the current time period, acquiring historical abnormal data of the data drift abnormity of each monitoring index, and acquiring the historical abnormal data influenced by the temperature under each monitoring index according to the own performance of each piece of historical abnormal data; according to the method, the relation curve of the temperature and the historical abnormal data influenced by the temperature under each monitoring index and the real-time data performance of each monitoring index in the current time period are integrated, the reason of the data drift abnormity of the electric energy meter in the current time period is determined, then fault detection is carried out on the electric energy meter, and the maintenance efficiency of the electric energy meter is improved.

An illumination device, system, and method are provided herein for emulating sunlight along a daytime or nighttime locus. Color temperature is determined using a first color temperature curve that includes data representative of a first color temperature as a function of a time of day. In response to a request to a change in brightness of one or more LED chains, a target color temperature output as a function of the current time of day is determined using a second color temperature curve based on the requested change in brightness. The second color temperature curve includes the target color temperature corresponding to the determined current time of day. The target color temperature output includes a lesser change in color temperature at solar noon than the change in color temperature at sunrise and sunset.

The invention relates to the technical field of Internet of Things, in particular to a high-speed rail environment monitoring method based on a multi-mode sensor, which comprises the following steps: acquiring measurement data of the multi-mode sensor for temperature, vibration and air pressure in real time, and constructing a state observation vector at the current moment; generating an initial parameter matrix of multi-modal dynamic Kalman filtering by using a generative adversarial network; correcting the state observation vector at the current moment based on improved multi-modal dynamic Kalman filtering to obtain a target state vector; and calculating an environment score according to the state value of each modal sensor in the target state vector at the current moment, evaluating the high-speed rail environment at the current moment, and taking corresponding response measures. According to the method, multi-modal sensor data can be integrated in a complex scene of high-speed running of the high-speed rail, quantified environment scores are output in real time, the running risk level is visually reflected, a reliable basis is provided for train dispatching, fault pre-judgment and emergency decision making, and the running safety and the operation and maintenance efficiency of the high-speed rail are remarkably improved.

The invention relates to the technical field of power distribution networks, in particular to a real-time monitoring method and system for multi-mode industrial Internet of Things data. The method comprises the following steps: acquiring the operation influence degree of each node on any other node at the current moment according to the fluctuation association degree of power indexes of each node and any other node in a monitoring time period in a virtual power plant and the similarity between the electric appliance connection tightness and the production state of the corresponding two nodes; according to the power index deviation degree and deviation time of each node in the virtual power plant relative to the state similar moment and the operation influence degree, obtaining the blocking resistance influence degree of each node at the current moment, and obtaining the response priority of each node in the virtual power plant at the current moment in combination with the power supply and consumption capacity; and determining the sequence of executing the virtual power plant dispatching instruction by each node. According to the method, line impedance influence and mutual influence between equipment are considered, so that the response priority is more reliable, and the operation toughness, safety and stability of the industrial park power distribution network are enhanced.

The invention relates to the technical field of data processing, and particularly provides a power supply health monitoring method, system and device and a storage medium, and the method comprises the steps: obtaining the real-time state data of a power supply; evaluating a health degree score of the battery according to the real-time state data; arranging the health degree scores generated in the current time window into a health degree score sequence; and utilizing a prediction model to predict the remaining service life of the battery according to the health degree. According to the method, the health degree score is constructed into the sequential sequence, and the prediction model is adopted for analysis, so that the prediction precision is improved, the working condition adaptability is enhanced, and the maintenance cost is optimized.

The invention provides a real-time scheduling method for a wireless network based on a large language model, the wireless network comprises a scheduling node and a plurality of user nodes, and the scheduling node is provided with a scheduling agent, a historical memory pool, an reflection agent and a suggestion buffer area. The method comprises the following steps executed in each time slot: step S1, the scheduling node acquires global information of a wireless network under the current time slot and calculates states of all data streams under the current time slot according to a preset calculation rule; and S2, the scheduling agent generates a scheduling decision of the current time slot according to a preset scheduling prompt word by using a large language model configured by the scheduling agent based on the states of all data streams under the current time slot and decision suggestions stored in the suggestion buffer area and by taking a maximized reward as a target. According to the technical scheme of the invention, by introducing the large language model, the problem of insufficient adaptability in a dynamic network environment in the prior art is solved.

The invention relates to the technical field of Internet of Things, and discloses a discrete reporting method, device and equipment based on NB communication, a medium and a product, and the method comprises the steps: generating the reference time after the NB node is powered on based on the current time of the NB node or the time of a meter corresponding to the NB node; generating seed time after the NB node is powered on by adopting a preset algorithm based on the meter address corresponding to the NB node; calculating discrete reporting time of each NB node based on the reference time, the seed time and preset grouping information of the NB nodes, and reporting data according to the discrete reporting time; and calculating a data reporting success rate, and dynamically adjusting the discrete reporting time of each NB node based on the data reporting success rate. The discrete reporting time is calculated based on the reference time, the seed time and the preset grouping information, so that network resources can be reasonably distributed, the pressure on the network caused by simultaneous data reporting of a large number of nodes in the same time period is reduced, and network congestion is avoided.

The invention provides a robot control method, a robot and electronic equipment, and the method comprises the steps: planning an initial action sequence of the robot according to the environment information of the robot, correcting a part of action sequences in the initial action sequence according to a target action state of the robot at a current moment, and obtaining a target action sequence; and finally, the robot is controlled according to the target action sequence, the collection moment of the environment information is before the current moment, the initial action sequence comprises initial action states of the robot at multiple moments after the collection moment, and the partial action sequence is the action sequence located after the current moment in the initial action sequence. According to the method, the two steps of planning the action sequence and executing the action sequence can be executed at the same time, and the control efficiency is ensured.

The invention relates to the technical field of directional advertisement, in particular to an advertisement putting method based on a user portrait, which comprises the following steps of: acquiring equipment use time of a target user, recording use types and corresponding use frequencies of software and corresponding advertisement response information, dividing the equipment use time into a plurality of putting characteristic time periods, determining the advertisement putting type of each putting characteristic time period; according to the advertisement feedback attitude of the user in each feature time period, determining an advertisement effective tendency category of the current user in each putting feature time period so as to determine a basic advertisement putting mode; and according to the duration percentage in each time period, determining an advertisement acceptance fluctuation parameter of the user in the current time period so as to judge whether to adjust the advertisement putting content in the current characteristic time period, and according to the advertisement watching duration percentage of each use type of software, determining an adjustment direction. And the overall advertisement putting effect is improved.

The invention discloses a variable frequency phase shift control DAB type single-stage inverter reactive power control method, system and device and a medium, and belongs to the technical field of power electronic converter control, and the method comprises the steps: setting a reactive power instruction as zero; when the reactive power instruction changes, the active power instruction and the reactive power instruction at the current moment are decomposed into a direct-axis current reference value and a quadrature-axis current reference value respectively; comparing the reference value with an actual sampling value, calculating an error compensation amount by using a PI controller, and determining the amplitude and phase of the AC side current according to the compensation amount; according to the amplitude and the phase of the voltage and the current, a DAB frequency conversion phase shift control curve is calculated; and generating a PWM (Pulse Width Modulation) signal according to the frequency conversion phase shift control curve obtained by calculation, wherein the PWM signal is used for driving a DAB inverter to carry out power transmission. According to the invention, on the premise that no extra hardware circuit is introduced, dynamic adjustment and accurate control of the instantaneous reactive power of the AC output side can be realized.

The invention belongs to the field of batteries, and particularly discloses a battery multi-parameter time alignment method, a battery feature extraction method and a battery anomaly detection method, and the time alignment method comprises the steps: obtaining the current time series data of a battery cluster, and the voltage time series data of each battery cell to which the battery cluster belongs; reconstructing the voltage time sequence data of each battery cell based on the current time sequence data of the battery cluster by adopting an improved DTW algorithm, so that the feature points of the reconstructed voltage time sequence data are aligned with those of the current time sequence data; a distance measurement matrix of the improved DTW algorithm is determined by a physical distance factor and a time distance factor; the physical distance factor is determined according to the current value of the current time sequence data, the voltage value of the voltage time sequence data, the open-circuit voltage of the battery cell and the equivalent internal resistance, and the time distance factor is determined according to the time of the current time sequence data and the voltage time sequence data. According to the invention, the operation and maintenance efficiency of the energy storage system is greatly improved.

The invention relates to the technical field of solid state disks, in particular to a data guarantee method and device for an open-state physical block, equipment and a storage medium. Comprising the following steps: determining a detection period of an open-state physical block based on NAND physical attribute parameters of the physical block; when the physical block is in the open state, recording an initial timestamp corresponding to the opening moment of the physical block and triggering a detection process; if the detection process is in the detection period, obtaining a current timestamp, and calculating a time difference between the current timestamp and the initial timestamp to obtain the opening duration of the physical block; calculating theoretical filling time according to the number of filled physical pages in the physical block and the detection period; and if the opening duration is greater than the theoretical filling time, writing virtual data into the to-be-filled physical page, otherwise, waiting for a next detection period to execute detection. According to the method, the limitation of fixed period operation is avoided, effective management of open physical block data is realized, and the use efficiency and reliability of the solid state disk are improved.

The invention discloses an event flow assisted link self-adaption method and system oriented to the Internet of Vehicles, and belongs to the field of link self-adaption of the Internet of Vehicles, and the method comprises the steps: obtaining event flow data; inputting the event stream data into the trained depth estimation model to obtain the environment depth under the current time step; calculating the distance between the base station and the user equipment based on the event stream data and the environment depth under the current time step; and based on the distance between the base station and the user equipment, estimating a channel state and calculating a coding rate under the current time step so as to complete link self-adaption. According to the invention, dynamic sensing of the distance between the BS and the UE is realized by using the space-time correlation of the event stream, the time delay defect of a traditional feedback mechanism is overcome, the problem of inaccurate channel acquisition caused by traditional outdated feedback information is solved through visual information, the model calculation complexity is reduced, the coding rate performance is improved, and the spectrum efficiency is further improved.