29 results about "Time scaling" patented technology

The invention discloses an authority management system of interactive equipment, particularly relates to the field of computer authority management, is used for solving the problem that an interface state is disjointed from an authority decision, and is characterized in that a time mark acquisition module is used for acquiring a role context, a process state and an interface operable item on a unified timeline to generate a context snapshot and writing the context snapshot into a time anchor point; the contract construction module establishes interface contracts based on context snapshots to perform homologous binding to form a constraint set, and the mapping evidence solidification module generates permission mapping table expansion constraints in a rendering stage and writes behavior evidence chain solidification presentation basis. The judgment evaluation module calculates a judgment credibility coefficient according to a time anchor point alignment operation event and an approval record to execute local judgment or record through generating a judgment record; and the version revision module executes a minimum change revision mark change on an interface contract according to the judgment record and releases a new permission mapping table to recycle an old version to form a reviewable sequence.

The invention discloses a 360-degree video edge caching joint bit rate allocation method based on double time scales, and belongs to the technical field of 360-degree video streaming and edge caching. The method comprises the following steps that: in a long-time scale period, an edge server collects and analyzes historical request data of a user, calculates a comprehensive value priority for candidate video slices according to long, medium and short-term popularity of the slices, and further generates an optimal caching strategy which takes effect in a next period. In a short time scale period, according to the caching strategy, the edge server processes the real-time requests of the user and distributes bit rates to all concurrent requests fairly and efficiently. Through a self-adaptive optimization mechanism combining long-term planning and real-time response, the high cache hit rate of high-value slices can be ensured in different storage spaces, the data request quantity to the cloud is reduced, the pressure of a return link is relieved, and the comprehensive experience quality of a user is remarkably improved.

This invention proposes a multi-energy safety assessment method and system considering multiple time scales and coupled faults. The method includes: establishing an intermediate time-scale model and a long-time-scale model of the integrated energy system; conducting a safety assessment for each fault scenario in a fault scenario set; wherein, using the long-time-scale model to perform an N-1 safety scan of the fault scenarios considering coupled faults, calculating system risk assessment indicators; when the system risk assessment indicators reflect that the fault scenario may lead to water hammer consequences, using the intermediate time-scale model to calculate the overpressure value generated during the water hammer process; and generating the final safety assessment result of the integrated energy system based on the safety assessment results of each fault scenario. This invention can capture the water hammer effect of the heating network and the cascading faults caused by the coupling of the logical and geographical layers, which are often overlooked in traditional assessments, achieving a more comprehensive and accurate safety assessment of the integrated energy system and helping to improve the operational safety of the system.

This paper relates to a generator set control method and device based on multi-timescale features. The higher-level scale provides guidance information to the lower-level scale; the weekly plan provides the unit start-up and shutdown status for the day-ahead plan, and the day-ahead plan provides the basic output points of the units for the intraday plan. At the weekly scale, the constraints of unit timing coupling can be ignored, and a dataset is generated by feeding back from the lower-level scale to the higher-level scale. This results in smaller data dimensionality and lower computational cost for data generation. Then, based on a neural network, the start-up and shutdown status of the units is predicted, and reliable start-up and shutdown statuses are selected and passed down, eliminating the need for feasibility remedial measures for start-up and shutdown statuses and reducing computational complexity. At the day-ahead and intraday scales, the start-up and shutdown statuses of the units determined by the higher-level time scale are used to identify more redundant constraints and accelerate the solution speed. This paper utilizes the features of different time scales to solve the unit combination problem, which can accelerate the solution speed while ensuring solution quality, and the solution speed is more stable.

The invention discloses a high-precision wave recording acquisition and time mark synchronization system for power grid fault positioning, and relates to the technical field of power grid wave recording synchronization, and the system comprises the steps: 1, synchronously outputting three-gear sampling under a time axis of the same coordinated world time, and generating a time uncertainty upper bound and a group delay signature for a channel and a fragment; 2, performing quality locking near-lossless on the high-energy fragment, after coding, checking the high-energy fragment to reach a sample position and a peak amplitude through homologous detection, and returning to be lossless if the high-energy fragment is unqualified; 3, writing a fragment index, quality metadata, a fragment verification structure and a whole file signature into the compatible single file expansion container; step 4, carrying out progressive retrieval, breakpoint resume and cross-station de-duplication according to indexes, and implementing cold and hot hierarchical storage; and 5, based on group delay signature compensation and quality weighted loading, solving a reference moment and generating an acceptance check list. According to the scheme, cross-device alignment, reproducible key features, transmission and storage peak clipping, playback and evidence obtaining caliber unification and tool compatibility and auditing are realized.

The invention discloses a physiological state monitoring method, device and equipment based on multi-time scale constraint and a medium, relates to the technical field of medical information processing and data analysis, is applied to a computer device, and comprises the following steps: determining a target physiological baseline value of each mode; obtaining a current physiological signal of each mode in a current monitoring time period, extracting a basic physiological feature under a multi-time scale sliding window from the current signal based on a consistency constraint mechanism, and generating a physiological change feature; analyzing cross-modal joint features according to the basic physiological features and the change features; the method comprises the following steps: establishing a state unit containing each feature, respectively organizing each state unit into a state sequence of a corresponding time scale to obtain a multi-scale physiological state sequence of a current monitoring time period, and adding the sequence to the end of a previous sequence to obtain a latest physiological state monitoring result. The pertinence and accuracy of physiological state evaluation and the collaborative characterization of multi-modal data are realized, and the continuity of full-cycle data is guaranteed.

The application discloses a wind speed prediction model training method and a prediction method based on multi-time scale fusion, relates to the technical field of computers, and the wind speed prediction model training method is characterized in that: initial data in a same time length is divided by different time scales to obtain first training samples and second training samples of different time scales; a first time scale prediction wind speed is obtained by performing feature extraction on the first training samples; average wind speed is obtained by performing data conversion on the first time scale prediction wind speed; a fluctuating wind prediction result is determined based on the first time scale prediction wind speed and the average wind speed; a second time scale prediction wind speed is determined by performing feature extraction on the second training samples; a prediction wind speed is determined based on the fluctuating wind prediction result and the second time scale prediction wind speed; the microfluctuation of wind speed is extracted according to the first training samples of the first time scale; and the macro trend of wind speed is extracted according to the second training samples of the second time scale, so that the accuracy of wind speed prediction can be improved.

The application discloses a kind of multi-agent preset time formation control methods based on backstepping method under distance rigidity matrix, comprising: 1) based on the double integrator dynamics model of multi-agent system is established based on rigidity graph theory, define the minimum and infinitesimal rigidity frame corresponding to the expected formation;2) introduce backstepping design framework, define virtual speed error variable;3) introduce time-varying scaling function to construct time scale transformation;4) design distributed preset time controller;5) through numerical simulation and physical platform are verified, verify the effectiveness of distance error in user preset time under different initial conditions converges to the vicinity of zero.The application is fused by backstepping and preset time scaling function, for the first time in only local relative measurement distance rigidity formation Convergence time user self-defined preset is realized, and convergence time upper bound is independent of initial condition and controller parameter, provides reliable, engineering implementation solution for time-critical multi-agent cooperative task.

The invention discloses a multi-time scale energy scheduling method and system based on electricity price prediction. The method comprises the steps of obtaining electricity price prediction values on multiple time scales in the future; generating an energy scheduling strategy on multiple time scales; executing an energy scheduling strategy on multiple time scales, monitoring an actual electricity price in real time, and comparing the actual electricity price with a corresponding electricity price prediction value to determine a prediction error; judging whether the prediction error is greater than an error threshold; in response to the fact that the prediction error is larger than an error threshold value, updating parameters of the electricity price prediction model according to the prediction error, and returning to the step of obtaining electricity price prediction values on multiple time scales in the future; and taking the current energy scheduling strategy on the multi-time scale as a final energy scheduling strategy on the multi-time scale in response to the fact that the prediction error is not greater than the error threshold value. By using the scheme of the invention, the electricity price trend in the short period, the middle period and even the long period in the future can be pre-judged, and a solid foundation is provided for formulating a comprehensive and fine energy scheduling strategy.

The application discloses a quad-rotor unmanned aerial vehicle attitude tracking double-time scale model predictive control method, relates to the quad-rotor unmanned aerial vehicle control technical field, and aims at the double-time scale characteristics and input constraint problems existing in the quad-rotor unmanned aerial vehicle attitude system. First, the singular perturbation theory is used to decompose the attitude system into two slow and fast reduced-order subsystems. Then, a model predictive controller matched with the time scale of each subsystem is designed for each subsystem, and a contraction constraint based on the auxiliary control law design is introduced in the optimization problem to ensure the recursive feasibility and closed-loop stability of the controller. Finally, the control amount of the two subsystems is combined to construct the composite control input of the attitude system. The application reduces the complexity of the quad-rotor unmanned aerial vehicle attitude system controller design through the decomposition-combination design strategy, and can explicitly process the input constraint while improving the attitude tracking performance and robustness through online optimization.

According to the precipitation grading method for obtaining any time scale, only 12-hour and 24-hour grading standards are given by the precipitation grading national standard (GB / T 28592-2012), and precipitation grading standards of other time scales are not given. However, many industries (such as traffic, hydrological monitoring and the like) possibly have requirements for precipitation division standards of other time scales (such as 10 minutes, 1 hour and the like), and in order to meet the requirements, an equal ranking rate method is provided, and the method can obtain precipitation division standards of any time scale not larger than the time resolution of precipitation data. Taking ERA5 hourly rainfall data and time scale 24-hour rainfall division standards (GB / T28592-2012) of Nanjing region (111 DEG E, 32 DEG N) as examples, the rainfall division standards of time scales of 1 hour, 3 hours, 6 hours and 12 hours are obtained by using the method, and the rainfall division standards of time scales of 12 hours obtained by using the method are close to the national standard. Therefore, if minute-level automatic station rainfall data is taken as a research object, the method can obtain a rainfall division standard of any time scale.

The application belongs to the technical field of intelligent education, and discloses a knowledge tracking method and system based on a cascaded time sequence feature processing architecture, which comprises the following steps: mapping historical answering interaction sequence data of students into a high-dimensional feature vector; performing deep processing on the high-dimensional feature vector to capture data features of different time scales; and predicting the performance of students based on the data features of different time scales. Through the collaborative design of the heterogeneous cascaded architecture and the selective state space model, the application improves the training stability and prediction accuracy of the knowledge tracking model, and realizes efficient use of linear time complexity and computing resources.

The application discloses a Kalman filter time scale establishment method considering abnormal data, and comprises the following steps: acquiring a time difference measurement value sequence of an atomic clock group, processing the time difference measurement value sequence to obtain atomic clock state variable solution information of all epochs in the time difference measurement value sequence, and calibrating an arbitrary single time-keeping clock by using the atomic clock state variable solution information to obtain a calibrated time scale. Compared with an existing time scale algorithm, the algorithm can directly eliminate the influence of atomic clock time difference observation data interruption and abnormal conditions on the time scale, and the generated time scale is continuous and stable.

The invention provides a new energy bearing capacity assessment method based on multi-time scale time sequence simulation of two-stage scene reduction, and belongs to the technical field of new energy bearing capacity assessment. The problem of complex calculation caused by multi-time scale coupling in new energy bearing capacity evaluation is solved; the method comprises the following steps: initializing; punishment is carried out on light abandoning, wind abandoning, water abandoning and load shedding by taking the minimum operation cost of the unit as an objective function; the operation constraint of the aggregated thermal power generating unit, the operation constraint of the pumped storage unit, the positive and negative spinning reserve constraint, the flexibility resource constraint, the load constraint and the power balance constraint are considered; constructing a two-stage scene reduction framework, and determining the number of reduced scenes and specific scenes in combination with absolute probability distance, relative probability distance and marginal relative probability distance indexes; establishing a comprehensive new energy bearing capacity evaluation index system to evaluate the power grid; the method is applied to the power system containing the new energy.

The invention provides an equipment joint maintenance optimization method and system based on a time window, which are applied to the technical field of equipment maintenance, and are used for establishing a joint maintenance time window under the same time scale by utilizing periodic and predictive maintenance time, identifying two types of equipment under the same time window, and carrying out unified maintenance. The method comprises the following specific steps: collecting operation state data of predictive equipment, and establishing a predictive maintenance plan; counting maintenance logs of periodic equipment, and establishing a periodic maintenance plan; constructing execution processes of two maintenance calculations under the same time scale; counting time intervals of predictive maintenance equipment and periodic maintenance equipment, and setting an average value of the time intervals as a threshold value of a combined maintenance time window; and judging whether the two maintenance calculations can be subjected to joint maintenance or not according to a threshold value. According to the equipment joint maintenance optimization method and system based on the time window, the downtime is reduced by unifying the equipment maintenance time, so that the production efficiency is improved.

The invention discloses a short-term thermal load prediction method, system and equipment based on time sequence rolling correction and a medium, and the method comprises the steps: collecting historical heat supply data and corresponding meteorological data, and carrying out the preprocessing, and obtaining reference data; analyzing the reference data through the maximum information coefficient to respectively obtain macroscopic prediction module parameters and microscopic prediction module parameters; the macroscopic prediction module parameters and the microscopic prediction module parameters are input into a day-ahead macroscopic prediction module and a day-time microscopic prediction module respectively, a day-ahead heat load prediction sequence and a key heat supply parameter value are obtained through prediction, and a day-time real-time heat load prediction value is obtained through calculation of the key heat supply parameter value; and carrying out weighted fusion on the corresponding predicted value in the day-ahead thermal load prediction sequence and the day real-time thermal load predicted value, and outputting a short-term thermal load predicted value. According to the prediction result, different time scales and multi-parameter characteristics are comprehensively considered, and the limitation of a traditional method in the aspects of prediction precision and system adaptability is solved.

The invention specifically discloses a health perception control method considering system performance degradation under double time scales, and belongs to the technical field of system health management. According to the method, a health awareness control (HAC) strategy under the double time scales is provided by constructing a degradation state and system state coupling model considering the double time scales and taking the degradation state as quantitative representation of the health condition. The strategy is based on model predictive control (MPC) multi-target collaborative optimization, on one hand, a dynamic penalty term of an output deviation change rate is introduced into a target function, and double-time-scale dynamic coupling of a fast-changing system state and a slow-changing degradation state is brought into an optimization problem, so that the performance of the system is optimized while the steady-state performance is achieved; and the problem that the control quantity fluctuates severely due to system degradation is effectively inhibited. And on the other hand, by estimating the degradation state, sensing the health condition of the system and constructing a dynamic adjustment weight factor, the system adapts to the influence of degradation on the system performance, and the degradation process of the system is effectively delayed.

The application provides a fault monitoring method and system for a numerical control machine tool, and particularly relates to the field of numerical control machine tool fault monitoring; by uniformly collecting and time window slicing processing of multi-channel running state time sequence data, a machine tool fault monitoring training set and a test set are constructed, and the dynamic correlation relationship of each monitoring channel under the same time scale is ensured; a spectral response value is constructed based on the ratio of the change amplitude of adjacent moments in the sliding window to the channel mean amplitude value, and a disturbance response value is formed by normalization compression; combined with the disturbance deviation intensity of the driving channel and the change direction sign of the response channel, a directional dependence structure between channels is analyzed and formed; further, a reverse interference reconstruction value and a strong residual quantity are constructed to form a channel-level fusion indicator, and a path tension indicator is constructed by sample difference extraction; on this basis, a path abnormality indicator is generated and weighted, and a machine tool fault detection value is output, so that stable detection and time sequence modeling of the fault state in the running process of the numerical control machine tool are realized.

The invention provides a space-time action detection method based on multi-scale time sequence characteristic interaction. The spatio-temporal motion detection aims to identify the time and location of motion occurrence and the category of the motion from an unclipped video. Most space-time action detection methods pay more attention to spatial modeling of actions and explore interaction between action figures and other objects in the environment. Modeling of time only stays in simple modeling of a single scale, which usually results in inaccurate and coherent prediction of long-term actions, because time information contained in the single scale is limited. Firstly, features of different time scales are obtained by establishing a time feature pyramid; and then, cross-scale time feature interaction is realized through an attention mechanism. In order to enhance the space interaction between the character features and the background features, the model obtains a larger-scale receptive field by introducing cavity convolution. According to the method, multi-scale interaction is realized in time and space, and the time boundary and the space position of the action are accurately predicted.

The application discloses a kind of forecast time coding methods based on large language model prior, belong to marine meteorological technical field.The application systematically encodes the periodicity and nonlinear variation law of marine state variable on different time scales into time embedding, improves the expression ability of time characteristics.The application utilizes the prior knowledge of large language model, optimizes the extraction and mapping process of time characteristics, improves the quality and efficiency of time embedding, converts time information into context semantic embedding, realizes accurate expression and efficient use of time characteristics.The application can improve the prediction accuracy, reduce error accumulation;Reduce model training and maintenance cost;Enhance the expression ability of time characteristics, improve the generalization ability of model;Improve the operation simplicity and practicality of prediction system;Optimize resource utilization, save energy consumption;Improve the reliability and stability of prediction system.

The invention discloses a multi-time scale demand scheduling method and system considering user response fatigue. The method comprises the following steps: firstly, constructing a user portrait based on historical data, and calculating an accumulated fatigue state covering accumulation and recovery processes in real time by using a dynamic quantitative model; secondly, constructing a fatigue-capacity physical mapping mechanism, and converting a fatigue state into an effective schedulable capacity upper limit; a multi-objective optimization model considering the system operation cost and the user fatigue loss is established; and finally, performing fatigue state updating and scheduling target compensation based on the actual response power of the user by adopting a rolling time domain control strategy. According to the method, user fatigue characteristics in a continuous calling scene can be accurately described, and through a closed-loop feedback correction mechanism, the economy of scheduling and the sustainability of resources are effectively considered while the supply and demand balance of a power grid is guaranteed.

The invention provides an article information prediction method and device, electronic equipment, a storage medium and a computer program product, and relates to the technical field of electronic commerce information prediction.The method comprises the steps that historical change information of each article in historical time windows corresponding to multiple time scales is input into a preset multi-scale fusion model, determining predicted change information of each article in the next time window; wherein each historical time window is a time window corresponding to the same time period with the next time window in the corresponding time scale. Therefore, when the prediction change information is determined, the historical change information in a plurality of time scales is considered, so that more and more reliable prior information can be provided for the preset multi-scale fusion model, the convergence speed of the model can be accelerated, and the prediction accuracy of the prediction change information is improved.

The application provides a machine learning prediction method and system for tuning load to an arbitrary time scale, relates to the field of power transaction, and comprises the following steps: obtaining historical load, meteorological and economic data, constructing a feature library, screening key features, and preprocessing to obtain preprocessed data; constructing a dimension separation model and performing training optimization to obtain a decision tree model; calculating SHAP values of each feature, analyzing the contribution degree of global features, and obtaining an analysis result; performing similar day matching and dimension fusion to obtain a fused load prediction result; performing multi-time scale hierarchical prediction to generate an arbitrary scale load curve; verifying the prediction bias through actual data backtesting, monitoring the data, and triggering incremental learning to form a closed loop optimization. The application is used to solve the accuracy requirement of boundary condition analysis in medium and long term and spot transaction.

The invention discloses a dual-time scale model prediction control method for attitude tracking of a quad-rotor unmanned aerial vehicle, relates to the technical field of quad-rotor unmanned aerial vehicle control, and aims to solve the problems of dual-time scale characteristics and input constraints in an attitude system of the quad-rotor unmanned aerial vehicle. The method comprises the following steps: firstly, decomposing an attitude system into a slow order reduction subsystem and a fast order reduction subsystem by using a singular perturbation theory; then, a model prediction controller matched with the time scale of each subsystem is designed for the subsystem, and contraction constraints designed based on an auxiliary control law are introduced into an optimization problem, so that the recursion feasibility and closed-loop stability of the controllers are guaranteed; and finally, combining the control quantities of the two subsystems to construct composite control input of the attitude system. The design complexity of the quadrotor unmanned aerial vehicle attitude system controller is reduced through a decomposition-composition design strategy, and the attitude tracking performance and robustness can be improved through online optimization while the input constraint is explicitly processed.

The invention provides a unified fixed time and preset time control method and system for USV-AUV cooperative trajectory tracking, and the method comprises the following steps: S1, task trajectory generation and modeling, S2, construction of a unified fixed time and preset time function, and giving of a stability condition, S3, state acquisition and time scaling error construction, and S4, state acquisition and time scaling error construction. The method comprises the steps of S1, establishing a first Lyapunov function and designing a virtual control law, S2, establishing a second Lyapunov function and designing a control law, and S6, establishing an overall Lyapunov function and analyzing preset time stability. According to the technical scheme, the control input can be always kept in an amplitude range which can be realized by a project, and unrealizable singular behaviors of the control gain at a specific moment are avoided, so that the main obstacle that a traditional preset time strategy is difficult to deploy in an actual system is overcome; and the engineering implementability of the method in an offshore operation scene is obviously improved.