824 results about "Time based" patented technology

An example operation may include one or more of executing a waiting period of time within a software application being accessed by a user, executing an animation via the software application during the waiting period, determining a result of the software application being accessed by the user, determining additional time to add to the waiting period of time based on the result, via the software application, executing the waiting period with the additional time, augmenting the animation based on the additional time to add to the waiting period, via the software application, and executing the augmented animation via the software application during the additional time. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

The invention relates to the technical field of thermal power plants, in particular to a thermal power plant load prediction method and system. The method comprises the following steps: acquiring real-time operation data of a thermal power plant, wherein the real-time operation data comprises historical load data, unit operation parameter data, environment parameter data and power grid dispatching data; performing data missing processing based on a time sequence interpolation method on the real-time operation data, and detecting and correcting an abnormal value to obtain operation preprocessing data; multi-scale time feature extraction is carried out on the operation preprocessing data to obtain time feature data, and multi-scale time features comprise hour features, day features and weekly features; and constructing a multi-level load prediction model based on the time characteristic data. According to the method, through multi-scale time feature extraction, load features of different time levels such as hours, days and weeks are comprehensively covered, and richer input information is provided for prediction.

Techniques for performing a machine learning model based spatial-temporal adaptive shift for end-to-end text-video retrieval are described. According to some examples, a computer-implemented method includes receiving a video comprising a plurality of frames at a content delivery service; generating, by the content delivery service, a set of embeddings for each of a plurality of sections of each frame of the plurality of frames; determining, by a candidate selector machine learning model of the content delivery service, a proper subset of the plurality of sections of each frame of the plurality of frames for a time shift based on the set of embeddings; time shifting, by the content delivery service, the proper subset of the plurality of sections of each frame of the plurality of frames to generate time shifted frames; generating, by the content delivery service, an updated set of embeddings based on the time shifted frames; receiving a search request comprising input text from a user device; determining the video is a match for the search request based on the input text and the updated set of embeddings for the time shifted frames; and sending the video to the user device based on the match.

The invention relates to the technical field of transformers, and provides a transformer load capacity prediction method and device, equipment and a storage medium, and the method comprises the steps: obtaining current data, winding temperature data, oil temperature data, environment temperature data and historical load records of a target transformer; calculating a temperature rise correction value based on the winding temperature data and the oil temperature data; after the temperature rise change rate in unit time is calculated through the current data, the environment temperature data and the temperature rise correction value, time sequence matching analysis is carried out on the temperature rise change rate and historical load records, after the load capacity change trend is obtained, comparative analysis is carried out on the load capacity change trend and the historical load records, and a load capacity prediction result is generated. By utilizing collaborative analysis and heat conduction characteristic calculation of multi-source data and comprehensively constructing a time delay interval and a load capacity evaluation method, the precision and applicability of transformer load capacity prediction are improved, and the problem that the prediction precision is insufficient due to the fact that operation data and environment data of the transformer cannot be fully fused during multi-source data processing is solved.

The invention discloses a method for predicting blast furnace TRT equipment maintenance time based on an LSTM model, and the method comprises the steps: constructing a sliding window adaptive algorithm based on vibration signal change characteristics, and dynamically adjusting the size of an input data sliding window of the LSTM model according to the fluctuation amplitude and change rate of a vibration signal; adopting an adaptive sliding window to obtain TRT equipment training data, and training the LSTM model; applying the trained LSTM model to the vibration signals acquired in real time, predicting the vibration trend of the TRT equipment in the future n days at the current point, and outputting a predicted value of the vibration signals in a future period of time; and according to the prediction result of the vibration signal, in combination with a preset threshold value of shutdown maintenance, judging the time when the TRT equipment reaches the threshold value, and further obtaining the remaining days of the TRT equipment away from the maintenance. According to the method, the future development trend of the TRT equipment vibration signal is accurately predicted, the maintenance plan is reasonably arranged, and the TRT equipment operation reliability and production efficiency are improved.

The invention discloses a logging-while-drilling reservoir parameter dynamic prediction method based on double attention convolution LSTM, and the method comprises the steps: obtaining logging-while-drilling data and drilling data in real time, and carrying out the preprocessing of the data; establishing a DTW dynamic time window model based on a gradual transition strategy, and adaptively adjusting the length of an input sequence according to the stratum change speed; a double attention convolution LSTM model is combined with the DTW dynamic time window model to establish a feature-time double attention mechanism, and two dimensions of features and time are adaptively fused based on the convolution LSTM; setting a buffer area, storing data and controlling data quality; setting an online increment fine tuning strategy; and performing instance training, and performing multi-angle evaluation on the performance of the double attention convolution LSTM model by adopting multiple indexes. According to the scheme, the influence of features and time on the prediction accuracy is fully considered, and the prediction accuracy is improved.

A system including a computing device configured to receive a time-based service record, generate a structured metadata object for the service record, the structured metadata including a plurality of programmatically assigned descriptors selected from a configurable schema, instantiate a dual-path processing routine as a function of the programmatically assigned descriptors, retrieve a constraint profile comprising at least one temporal delivery threshold associated with the resource allocation output and the utilization performance evaluation, aggregate time-based service records over a reporting interval as a function of the structured metadata, compute, using a time-mapping module, a constraint deviation metric as a function of comparing the time-based service records and the temporal delivery threshold, and generate an output object as a function of the constraint deviation metric.

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.

Method includes receiving a first delay time and a first timing measurement by a TMV from a first receiver at a first node. The first timing measurement is generated by comparing a first time associated with a first signal to a first local clock time. The first signal is transmitted by a transmitter at a source. The TMV receives from a second receiver at a second node in a network, a second delay time and a second timing measurement. The second timing measurement is generated by comparing a second time associated with a second signal to a second local clock time. The second signal is transmitted by the transmitter at the source. The TMV generates a correction factor time based on the first timing measurement, the first delay time, the second timing measurement, and the second delay time. The TMV transmits the correction factor time to the second node.

The invention provides a flexible load-oriented day-ahead intra-day inter-group secondary cooperative regulation and control method, and belongs to the technical field of power flexible load scheduling control. The problem that active participation of user side flexible resources in power grid regulation and control is uncertain is solved; comprising the following steps: S10, obtaining related characteristic parameters and historical electric quantity data of user side flexible resources; s20, establishing a flexible resource evaluation index, dividing flexible resources into different flexible load clusters through an improved K-means clustering method, and forming a layered regulation and control framework of flexible loads after aggregation; s30, in combination with a day-ahead load prediction method, calculating the maximum adjustable capacity of user side flexible resources, and in combination with a parallel Markov chain model, preliminarily making a flexible load day-ahead scheduling plan; s40, tracking the load power change of the user side in the day in real time, and correcting the adjustment capacity in time based on a flood filling algorithm to realize secondary cooperative regulation and control of the flexible load cluster; the method is applied to a power system.

An autonomous futures contract orchestration platform includes a set of processors programmed with a set of non-transitory computer-readable instructions. The instructions include receiving, from a data source, an indication associated with a product that relates to an entity that purchases or sells the product. The instructions include predicting a baseline cost of purchasing or selling the product at a future point in time based on the indication. The instructions include retrieving a futures cost, at a current point in time, of a futures contract for an obligation to the purchasing or selling the product for delivery or performance of the product at the future point in time. The instructions include executing a smart contract for the futures contract based on the baseline cost and the futures cost. The instructions include orchestrating the delivery or performance of the product at the future point in time.

A method includes receiving, by a computing device, first data from a sensor, the first data including a first timestamp and an event that triggered an alarm; receiving, by the computing device, second data from a remote computing environment, the second data including a second timestamp different from the first time stamp and specifying an action taken in response to the alarm; and rendering a screen on a display of a computing device, the screen including the first and second data represented as a sequence of events ordered by time based on the first and second timestamps.

The invention discloses a video courseware generation method and device based on artificial intelligence, computer equipment and a storage medium, and the method comprises the steps: generating a courseware page through employing an AIGC technology based on the content inputted by a user; extracting explanation words from the copywriting content of the courseware page, and synthesizing a courseware explanation video based on the explanation words, the audio selected by the user and the digital human image; based on the duration of the courseware explanation video, adding a time axis to the courseware page, and associating each courseware element of the courseware page with the time axis to obtain a courseware page based on time driving; and playing the courseware explanation video, driving the courseware page based on time driving based on the playing time of the courseware explanation video, and meanwhile, carrying out screen recording and packaging to generate scrom standard video courseware. Through the AIGC technology, the high-quality courseware content can be quickly generated, the courseware content production efficiency is improved, and the problems that existing courseware production is long in production period and high in labor cost are solved.

The invention relates to a shared charging control method and system based on the Internet of Things. The method comprises the following steps: determining the monitoring frequency of each charging pile in each time period by using historical data; judging whether the charging pile equipment is abnormal or not according to the state information of the charging pile in the monitoring time period; and when the current state type of the charging pile is an idle state, triggering a parking space monitoring instruction, and updating credit points of a vehicle parked on a parking space corresponding to the charging pile according to a monitoring result of the parking space monitoring instruction and whether the charging pile equipment is abnormal or not. Whether the charging pile equipment is abnormal or not can be found in time based on the use condition of the charging pile, the parking space occupation problem of the non-charging vehicle is further divided, the parking space with the abnormal charging pile equipment is fully utilized, credit points of the vehicle maliciously occupying the charging parking space are deducted, and the charging efficiency is improved. And full utilization of the charging pile is effectively ensured.

The invention discloses a predictive maintenance algorithm for transmission equipment, and relates to the technical field of maintenance of the transmission equipment, in particular to a roller conveying system applied to flexible materials, which is characterized by comprising the following steps: S1, arranging position triggers at key transition nodes of a roller conveyor for acquiring timestamps of the materials passing through detection points in real time, generating a unique number and a corresponding time sequence relationship for each material; and S2, standard material passing time is calculated based on the theoretical beat period, the difference between the actual time and the standard period is calculated, and a beat offset set is constructed to reflect the conveying beat deviation of the materials between the adjacent detection points. According to the method, early warning and intelligent regulation and control of flexible material conveying rhythm imbalance are achieved through rhythm offset modeling, abnormal aggregation recognition and root cause diagnosis, compression bandwidth and driving time delay are dynamically adjusted when the imbalance risk occurs, the accumulation risk is effectively relieved, and the system stability, the automation level and the operation efficiency are improved.

One or more computer processors may be communicatively coupled with a display of a vehicle and may process data. Using the processed data a vehicle system may display, on one or more user interfaces displayed on the display of the vehicle, a visual summary of a defined route for a trip. The visual summary may include a start indicator representing a starting location of the defined route, an end indicator representing an end location of the defined route, timeline drawn between the start indicator and the end indicator, and a progress indicator displayed on a location between the start indicator and the end indicator corresponding with a time-based completion percentage of the trip. In some cases the timeline may be depicted as a straight line. In some cases the one or more user interfaces may exclude a geographic map. The one or more user interfaces may exclude a distance indicator.

A method, system, and article of manufacture for modifying a queue assignment for a contact center are provided. At least one computing device determines, by solving for constraints, at least one staffing change for at least one of the first queue or the second queue, using as inputs (a) first time-based service metrics, (b) second time-based service metrics, (c) data describing eligible staffing changes, and (d) at least one predetermined constraint. The at least one staffing change is a modification to the queue assignment of a subset of the computing device instances that is predicted to improve net staffing relating to service targets selected from (a) an average speed of answer (ASA) specifying an average time for one of the computing device instances to initiate handling of a particular task after the particular task has been assigned to the relevant queue and (b) a service level specifying a percentage of tasks for which handling is initiated within a specified time frame after the tasks are assigned to the queue. The at least one staffing change can be implemented by modifying the queue assignment of the subset of the computing device instances.

This application discloses a communication method. The method includes: A terminal device obtains a first message, where the first message carries target adjustment information. The terminal device obtains a second message, where the second message carries a timing advance (TA). The terminal device adjusts uplink sending time based on the target adjustment information and the TA. Embodiments of this application further provide a corresponding terminal device, network device, and storage medium.

The invention discloses a method and a system for calculating driver service order duration in real time based on Flink, particularly relates to the technical field of data processing, and is characterized in that a window operation of the Flink is used for defining a vehicle receiving and dispatching event and an order event of a driver in a business data source as an input event stream, and an event type field is defined for distinguishing the vehicle receiving and dispatching event from the order event; according to the driver ID and the order ID, dividing time slices, in each time slice, performing grouping according to the driver ID and the order ID, calculating the service duration of each order of each driver in each time slice, and summarizing the driver service duration of each order of each slice; and aggregating the maximum service duration in each time slice according to the driver ID to obtain the service order duration of each driver in any time period, setting a threshold value of the fatigue driving duration of the driver, judging whether the threshold value is exceeded or not according to the service order duration data of the driver in any time period, and evaluating the fatigue driving condition of the driver.

The embodiment of the invention provides a clock phase demodulation method and a molecular clock, and the method comprises the steps: determining a demodulation phase initial value of an Nth harmonic based on an Nth harmonic signal outputted by the clock; wherein the N is a positive integer and is an odd number; determining a first phase change value of the Mth harmonic signal along with time based on the Mth harmonic signal output by the clock; determining a second phase change value, changing along with time, of the L-th harmonic signal based on the L-th harmonic signal output by the clock; wherein M and L are positive integers and are even numbers; calculating a target demodulation phase based on the demodulation phase initial value, the first phase change value and the second phase change value; wherein the target demodulation phase is a demodulation phase used under the condition that frequency locking is carried out by adopting Nth harmonic waves. Through the method, the calculation amount of phase demodulation of the clock can be reduced.

An example operation includes one or more of assigning a time slot to a vehicle for a charging operation at a charging station, where the time slot includes a starting point in time and an end point in time, transmitting a notification to the vehicle which includes an identifier of the charging station and the starting point in time, iteratively capturing sensor data at the charging station, determining whether the charging operation started on time based on the starting point in time and the sensor data, and in response to a determination that the charging operation started on time, delivering content to the vehicle while the charging operation is being performed.

In a communication method, a terminal device sends uplink data corresponding to M HARQ processes, and receives first indication information, where the first indication information indicates whether to start a DRX uplink retransmission timer corresponding to N HARQ processes in the M HARQ processes; and the terminal device starts the DRX uplink retransmission timer when the first indication information indicates to start the DRX uplink retransmission timer, and receives second indication information when the DRX uplink retransmission timer runs, where the second indication information indicates retransmission of uplink data corresponding to the N HARQ processes. The terminal device can determine, in time based on the first indication information, whether to start the DRX uplink retransmission timer. A terminal device is configured with computer instructions to perform the described method.

The invention relates to the technical field of machining scheduling optimization, and discloses a material optimization scheduling method for machining. The method comprises the following steps: collecting production and material consumption data of a workshop in past 30 days, and predicting by using an ARIMA model based on historical data; a scheduling priority is distributed for each production process, the material flow condition of each process is monitored, and material supply of low-efficiency processes is automatically identified and adjusted. Compared with the prior art that production demand changes cannot be dynamically responded, and particularly under the condition that demand fluctuation is large or production procedures are complex, an existing scheduling system is difficult to achieve real-time adjustment, so that production efficiency is low, and resources are wasted. According to the method, the demand prediction and priority scheduling algorithm based on the time sequence is introduced, and the real-time material flow monitoring is combined, so that the dynamic optimization scheduling of the material supply is realized, the problem that the material supply is delayed is avoided, and the production efficiency and the resource utilization rate are improved.

The invention discloses a refrigerator control method and device, a refrigerator, electronic equipment and a storage medium. The method comprises the following steps: detecting a temperature sensor fault; the current operation condition corresponding to the fault sensor is determined, a temperature adjusting function of the refrigerator under the current operation condition is matched at a cloud end and serves as a target function, the cloud end carries out fitting according to normal operation data of refrigerators of the same model to obtain temperature adjusting functions under different operation conditions, and the temperature adjusting functions are used as the target function. The temperature adjusting function is used for representing the relation between a temperature parameter and time, and the temperature parameter is the chamber temperature or the defrosting temperature; calculating target time based on the target function; and after the refrigerator is controlled to operate for the target time under the current operation condition, entering a new operation condition and re-matching the target function, and repeating the steps until the fault sensor returns to normal. The refrigerator can be accurately controlled to operate on the basis of the temperature adjusting function when the refrigerator temperature sensor breaks down.

The invention relates to a software risk detection method and computing equipment, and relates to the technical field of software detection. The method comprises the following steps: acquiring a software material list of to-be-detected software; and determining risk indicator information of the open source module based on the software material list, wherein the risk indicator information comprises a software license risk indicator, a software security risk indicator and a software maintenance risk indicator of the open source module. And determining a risk detection result of the to-be-detected software based on the risk index information of the open source module. According to the method provided by the invention, the risk existing in the open source module of the to-be-detected software can be fully and accurately detected from multiple dimensions according to the risk index information of the open source module in the to-be-detected software, so that the risk detection result of the to-be-detected software is obtained, and the software testing efficiency can be improved. Moreover, in the software development stage, the technicians can repair the risk of the open source module of the software in time based on the risk detection result, so that the maintenance cost of the software can be reduced.

The embodiment of the invention provides an embedded operation method and system based on preemptive multi-task scheduling, and belongs to the technical field of multi-task scheduling. The method comprises the following steps: initializing a global task priority group, a start-stop bit field group and an execution quality feedback table, and configuring parameters of a time base interrupt source; loading task parameters, and generating a task configuration table; driving each task cycle counter to accumulate through the time base interrupt source, and when the current accumulated value of the cycle counter is matched with the cycle parameter in the task configuration table and the start-stop bit field group is started, generating a ready mark graph; updating the global task priority group, determining a to-be-executed task, and completing task scheduling; and executing the to-be-executed task, calculating a task priority compensation value, and adjusting the priority value of the corresponding task in the global task priority group in the next period based on the task priority compensation value. According to the method, the defect of static scheduling accumulative errors is overcome through a dynamic priority compensation mechanism.

The invention discloses a turbine service life prediction method, system and device and a storage medium, and relates to the technical field of engines, and the method comprises the steps: fitting a stress spectrum according to the actual working rotating speed spectrum of a turbine and the relation between the rotating speed and the stress; calculating the total fatigue damage at the blade root of the turbine blade after the target working duration based on the stress spectrum; calculating the total creep damage at the blade root of the turbine blade after the target working duration based on the stress spectrum, the rotating speed spectrum of actual operation and the temperature spectrum; and predicting the comprehensive life of the turbine according to the total fatigue damage and the total creep damage. According to the method, the fatigue creep comprehensive life prediction method under the given actual road spectrum is constructed, the working life of the supercharger turbine can be predicted more accurately by comprehensively considering various influence factors of the turbine under the actual working condition, and therefore the performance and safety of the turbine are improved, and stable operation of the turbine is guaranteed.

The invention relates to the technical field of time schedule controllers, and discloses an on-off control method of a time schedule controller, which comprises the following steps: electrically connecting the time schedule controller to a power supply, and starting the time schedule controller through an ON / OFF key to enable the time schedule controller to be in a standby state; then receiving a control mode selected by a user and input control parameters; then, a corresponding injection molding signal input mode is determined according to the type of an injection molding machine, and a proper output voltage form is selected according to the type of an electromagnetic valve; and finally, after an injection molding signal sent by the injection molding machine is received, the time sequence controller automatically controls opening and closing of the pouring gate based on the selected control mode and parameters. Particularly, the time schedule controller collects environment temperature data in real time based on a temperature sensor, and dynamically adjusts the frequency of a clock crystal oscillator to compensate clock drift, so that the stability and accuracy of on-off control of the time schedule controller are ensured.

The invention discloses a passenger flow staying duration calculation method and device, equipment and a medium. The method comprises the steps that a target time period input by a user is acquired; time segments in a target time period are determined based on a generation timestamp of a passenger flow change record, the passenger flow change record is a record generated when passenger flow change is detected, the passenger flow change comprises entering and leaving of people, and the passenger flow change record records the number of people entering and / or leaving; the time segment is a time segment determined by two adjacent generated timestamps; the total customer staying duration corresponding to the target time period is determined based on the customer staying duration of the time segment, the customer staying duration is determined by the number of staying customers of the time segment and the duration of the time segment, and the number of staying customers is determined based on the customer change record; and determining the average staying duration of the customers by using the total customer staying duration and the total number of people entering the store in the target time period. In this way, the average staying duration of the customer can be accurately determined under the condition that the privacy of the customer is guaranteed.