133 results about "Look-ahead" patented technology

The structures and algorithms described herein employ look ahead motion planning, in which motion planning for a least two goals is performed before a robot executes the resulting motion plans, and the ability to transition between the preceding one of the motion plans to a subsequent (e.g. following) one of the motions plans is assessed. Thus, the system can determine whether a robot will get trapped (e.g., blocked by another robot) at the end of a first motion plan, preventing, limiting or delaying execution of a second motion plan. Detection of such a condition can cause one or more remedial actions can be taken, for example generating a new, revised or replacement first motion plan. Other remedial action can moving another robot, performing motion planning for the other robot, to alleviate a blocking condition and / or determining a new order for the goals.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

The invention belongs to the technical field of electric power, and particularly relates to a short-time frequent jump current event clustering identification method based on a collector. The method comprises the following steps: step 1, carrying out current sampling on a monitored line, carrying out point-by-point amplitude difference comparison and adjacent waveform curvature judgment, and removing isolated burrs to obtain a jump characteristic fragment sequence; 2, traversing the jump characteristic fragment sequence in the forward direction, and generating a short-time frequent jump current event under the conditions that no less than three jumps in the same direction are satisfied and the time interval between fragments is within a preset range; 3, a look-ahead chain, a current chain and a review chain are established for the event index sequence column at the same time, and a clustering index set is output; 4, counting the occurrence frequency of each cluster index in the cluster index set; and determining that the short-time frequent jump current event corresponding to the clustering index is an effective event. According to the invention, the detection rate and the accuracy in a noise environment are improved.

The invention provides a new energy power grid prospective scheduling method and device, and relates to the technical field of electric power system intraday economic scheduling. The method comprises the following steps: firstly, constructing an opportunity constraint optimal power flow model of prospective scheduling, analyzing the influence of new energy uncertainty on opportunity constraint, establishing a constrained Markov decision process of prospective scheduling, and then utilizing a risk evaluator network fitting risk function probability distribution and an actuator network considering extreme scene performance to determine the opportunity constraint optimal power flow model of prospective scheduling. The processing capability of the intelligent agent on a prospective scheduling scene containing a new energy extreme climbing event is enhanced; and finally, the training of the intelligent agent is accelerated by utilizing an imitation learning technology in a power grid prospective scheduling off-line simulation environment. According to the method, the solving speed and the strategy robustness and safety of the double-layer robust optimization model of the look-ahead scheduling can be considered.

Systems and methods for online augmentation for learned grasping are provided. In one embodiment, a method is provided that includes identifying an action from a discrete action space. The method includes identifying a second set of grasps of the agent utilizing a transition model based on the action and at least one contact parameter. The at least one contact parameter defines allowed states of contact for the agent. The method includes applying a reward function to evaluate each grasp of the second set of grasps based on a set of contact forces within a friction cone that minimizes a difference between an actual net wrench on the object and a predetermined net wrench. The reward function is optimized online using a lookahead tree. The method includes selecting a next grasp from the second set. The method includes causing the agent to execute the next grasp.

The invention relates to the technical field of intelligent manufacturing and production optimization scheduling, and particularly discloses a thermal fuse flexible production-oriented order scheduling resource scheduling optimization method, which comprises the following steps of: generating a structured vector by extracting inherent and dynamic characteristics of an order, and constructing a production scheduling dynamic environment model by fusing a device resource state time sequence; the order comprehensive process switching cost and the resource constraint tightness are calculated, order clusters are divided through multi-target constraint clustering, and a pre-arrangement sequence is generated; key resource conflict early warning is completed through time sequence prediction; constructing a multi-target scheduling model by taking the order cluster as a unit, and generating a look-ahead window scheme by combining a rolling optimization solidification plan baseline; and finally, on the basis of a real-time execution data updating model, through dynamic priority preemption and local rescheduling adjustment when abnormity occurs, the problems of high cost of small-batch mixed arrangement, conflict identification lagging, easy damage of a thermosensitive process steady state and poor compliance constraint adaptation are solved, and collaborative optimization of the scheduling efficiency, the production yield and the compliance is realized.

The invention provides an intelligent control method and system for discrete event simulation of an aluminum template production line, and relates to the technical field of data processing.The method comprises the steps that a material transfer path instruction and a cutting process key point coordinate sequence are analyzed from a dynamic anti-interference scheduling strategy, the key point coordinate sequence is input into a curve driving algorithm, and a curve driving algorithm is obtained; a parameterized continuous cutting path curve is generated according to the aluminum template contour precision requirement and the cutting head dynamics constraint; the method comprises the following steps: performing equal-bow-height error discretization processing on a continuous cutting path curve to generate a discrete pose point set with a timestamp, superposing AGV dynamic constraints to generate a speed look-ahead control instruction set, splitting the instruction set into independent control units according to process sections, packaging the independent control units into a time trigger message through an industrial bus protocol, and sending the time trigger message to a server; and the simulation clock synchronization signal is injected into a physical execution unit. According to the invention, intelligent and accurate control of the aluminum template production process is realized, and the production efficiency is effectively improved.

The invention discloses an AGC instruction anti-error method and system based on digital twinning and multi-risk look-ahead, and belongs to the technical field of automation and control of power systems. The method comprises the following steps: constructing a digital twinborn body which operates synchronously with a physical power system AGC control loop; acquiring operation state data; receiving an original control instruction from a superior AGC system; performing look-ahead simulation on the execution effect of the original control instruction to obtain a system state track prediction value; calculating five-dimension risk indexes by using a risk quantitative evaluator; judging whether any risk index of the five-dimension risk indexes exceeds a preset threshold value or not; if yes, triggering the instruction safety correction module, and executing the next step; if not, directly issuing the original control instruction to a station physical execution unit; an instruction safety correction module is started, rolling optimization is carried out on the original control instruction, and a safety instruction is generated; and issuing the security instruction to a physical execution unit.

The invention discloses a look-ahead sliding decision feedback equalizer and a Serdes receiver, and the look-ahead sliding decision feedback equalizer comprises a two-stage assembly line which is composed of an m-tap adder and an n-tap decision device based on a look-ahead structure. Each tap of the n-tap decision device comprises a three-decision device unit 3T-D, a four-decision device unit 4T-D and a five-decision device unit 5T-D, the four-decision device unit 4T-D is a one-out-of-four multiplexer 4-to-1 MUX used for performing look-ahead calculation, the five-decision device unit 5T-D comprises two one-out-of-four multiplexers 4-to-1 MUX used for performing look-ahead calculation, and the four-decision device unit 4T-D is a one-out-of-four multiplexer 4-to-1 MUX used for performing look-ahead calculation. And a judgment result output by the three-decision-device unit 3T-D is used as a selection signal to act on the four-decision-device unit 4T-D and the five-decision-device unit 5T-D. The invention aims to efficiently process data from the feed-forward equalizer and reduce the delay of a critical path in a decoding part of the sliding decision feedback equalizer.

The invention provides a multi-station cooperative control method based on event triggering and look-ahead synchronization. The multi-station cooperative control method comprises the following steps: S1, defining nominal motion parameters for each station; s2, when the physical completion event of the current station is detected, calculating to obtain an accumulated time deviation up to the current station; s3, on the basis of the cumulative time deviation, executing online optimization calculation in a prediction time domain containing a plurality of stations in the future so as to determine an optimal target operation duration used for compensating the cumulative time deviation; and S4, according to the optimal target operation duration, generating and issuing a new motion curve for a first station in the prediction time domain, taking the station as a new current station, and circularly executing the steps S2 to S4. According to the method, the system can actively eliminate tiny time deviation caused by uncertainty factors such as material difference and mechanical wear, and the accumulative effect of errors is avoided.

The invention discloses a robot polishing process regulation and control method based on deep learning, and relates to the technical field of workpiece machining. The regulation and control method comprises the steps that S01, the to-be-polished surface of a workpiece is divided into a plurality of areas, attribute division is conducted on the areas, the areas with the same attribute are divided into the same group, and a plurality of area groups are obtained; s02, establishing a corresponding surface parameter standard range for the same group of areas; by introducing the deep learning-based look-ahead prediction method, the change trend of multi-dimensional information data in the polishing process can be analyzed in real time, physical roots (such as polishing tool abrasion and temperature accumulation) are diagnosed in advance based on the associated network diagram, a prediction adjustment instruction is generated, active compensation optimization of a subsequent polishing program is achieved, and the polishing efficiency is improved. The lagging of post adjustment is effectively overcome, the efficiency and quality consistency of grinding operation are remarkably guaranteed, and meanwhile time waste caused by re-grinding is reduced.

The invention relates to the technical field of electric digital data processing, and discloses a data processing system for message management, which comprises a look-ahead buffer unit for maintaining a sliding window with variable window size parameters and pre-reading a message sequence; the mapping logic circuit is used for converting the resource identifier into a resource request mask; the state register is used for storing a global occupancy bitmap for representing resource occupancy; the scheduling controller is used for distributing messages according to bit operation results, counting bitmap setting quantity based on the population counting instruction to generate a resource saturation index, and then adjusting window size parameters in a negative feedback mode. The resource contention entropy value is directly sensed by utilizing a hardware instruction, nanosecond-level self-adaptive breathing control of a scheduling window is realized, and the service life of the scheduling window is prolonged. The problem of physical computing power collapse caused by bitmap scanning jitter in a high-concurrency scene is solved.

A video bitstream is received. The video bitstream includes coded information of a current block in a current picture and of a plurality of reference pictures of the current picture in a reference list. A plurality of intermediate vectors associated with one of the plurality of reference pictures is determined. The plurality of intermediate vectors includes an initial vector and a plurality of intermediate motion vectors (MVs). The initial vector is associated with the current picture. Each of the plurality of intermediate MVs is defined between two respective reference pictures of the plurality of reference pictures. A candidate motion vector predictor (MVP) is determined for the current block based on a sum of the plurality of intermediate vectors. The current block is reconstructed based on an MVP candidate list that includes the candidate MVP.

The invention belongs to the technical field of intelligent building and heating ventilation air conditioner control, and particularly discloses an air conditioner system look-ahead optimization control method based on rolling prediction and global collaboration, which comprises the following steps: performing preprocessing and feature construction on air conditioner system data to obtain a historical data window; inputting the window into a Transform prediction model to obtain a future prediction sequence; outputting a group of global optimal set values through an optimization strategy generator, calculating an initial set value by the optimization strategy generator by adopting an empirical formula group on the basis of a prediction sequence, and carrying out iterative solution by taking the initial set value as a starting point and based on a system total energy consumption model of a physical coupling relationship between fusion equipment, so as to obtain the global optimal set value enabling the total energy consumption of the system to be the lowest; and issuing the global optimal set value to an equipment controller for execution, and updating the Transform prediction model according to the actual operation data. The problem of large inertia lag of the air conditioning system can be solved, and accurate and reliable prospective energy efficiency optimization is achieved.

The invention relates to the technical field of path planning, and discloses a path tracking method and system based on prospective optimization and curvature self-adaption, a controller and a readable storage medium, and the method comprises the steps: obtaining the current pose and global path information of a target robot, and transferring the current pose and global path information to a vehicle body coordinate system; calculating a real-time look-ahead distance of the target robot according to the global path information to determine a look-ahead point; calculating a position error and an angle error according to the look-ahead point, and calculating a real-time PID parameter according to the position error and the angle error; the real-time path curvature is calculated according to the look-ahead point, the maximum linear speed is determined according to the real-time path curvature, and the current speed and the motion direction of the target robot are determined according to the maximum linear speed and the real-time PID parameters. According to the invention, through combination of the double PID controllers and the pure tracking algorithm, high-precision path tracking of the omni-directional mobile robot is realized.

Systems and methods herein are for a video encoder to be associated with a temporal filter and a coding tree and that can perform a main pass for video encoding using individual video blocks towards prediction of at least one frame associated with the media stream, where the coding tree is associated with a lookahead pass, and where the temporal filter can enable denoising within the lookahead pass to reduce an effect of noise in one or more of motion estimation or mode selection of the video encoding.

A video bitstream is received. The video bitstream includes coded information of a current block in a current picture and of a plurality of reference pictures of the current block in a reference list. The coded information indicates that the current block is coded in an affine mode. A first control point motion vector (CPMV) of a first control point of the current block is determined. The first CPMV is associated with a sum of a plurality of intermediate vectors. The plurality of intermediate vectors includes an initial CPMV and an intermediate motion vector (MV). The initial CPMV is between the current picture and an initial reference picture of the plurality of reference pictures. The intermediate MV is between two respective reference pictures of the plurality of reference pictures. The current block is reconstructed based on the first CPMV of the first control point of the current block.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate instructions for performing a next action of a task. For instance, in some cases, the disclosed systems receive, from a client device interacting with a software application, a query for performing a task via a user interface of the application. The disclosed systems generate a lookahead prompt having an execution example corresponding to the task, the execution example including an example task and an example action sequence for the example task. The disclosed systems also generate, from the lookahead prompt using a large language model, an estimated lookahead plan describing one or more actions for performing the task. The disclosed systems also use one or more large language models to generate, from the estimated lookahead plan, instructions to perform a next action for the task via user interaction with an interactive element of the user interface.

A video bitstream is received. The video bitstream includes coded information of a current block in a current picture and of a plurality of reference pictures of the current block in a reference list. The coded information indicates that the current block is coded in an affine mode. A first control point motion vector (CPMV) of a first control point of the current block is determined. The first CPMV is associated with a sum of a plurality of intermediate vectors. The plurality of intermediate vectors includes an initial CPMV and an intermediate motion vector (MV). The initial CPMV is between the current picture and an initial reference picture of the plurality of reference pictures. The intermediate MV is between two respective reference pictures of the plurality of reference pictures. The current block is reconstructed based on the first CPMV of the first control point of the current block.

The invention provides a special equipment detection scheme generation method and system, equipment and a medium, and belongs to the technical field of special equipment and artificial intelligence application, and the method comprises the steps: collecting data of target equipment to construct a data file, employing LSTM, CNN and Transformer encoders to extract time sequence, image and text features respectively, and fusing the features to obtain a detection scheme; intelligent evaluation of the equipment is realized; based on the evaluation result, generating an initial test scheme in combination with rule constraint and historical case recommendation; performing simulation prediction on the scheme through a digital twinborn environment, and performing multi-objective optimization by adopting reinforcement learning to obtain a safe, economic and efficient-balanced optimal test scheme; and finally, outputting the scheme, and performing feedback updating on the evaluation model according to an actual test result to form a closed-loop learning mechanism. According to the method, the test scheme is converted from experience driving to data intelligent driving, and the capabilities of personalized formulation, look-ahead optimization and sustainable evolution are improved.

The invention discloses a multi-agent priority obstacle avoidance path planning method for a dynamic obstacle, and relates to the technical field of mobile agent path planning, the method comprises the following steps: a global planning stage: optimizing an A * algorithm by introducing a heuristic scaling factor, an obstacle and direction penalty term and a neighborhood adaptive extension mechanism, a path is smoothed by combining broken line reconstruction and a Bezier curve; in the local planning stage, the weight of a DWA evaluation function is dynamically adjusted through fuzzy control, and the real-time obstacle avoidance robustness is improved. Meanwhile, a priority obstacle avoidance strategy and a dynamic look-ahead mechanism are designed, multi-agent conflicts are coordinated, and collective stagnation is avoided. Simulation results show that the method shows better path length, search efficiency and obstacle avoidance performance in static, unknown static and dynamic obstacle environments, effectively improves the multi-agent coordination stability, and is suitable for actual scenes such as warehouse logistics and the like.

The invention provides a prospective interpolation method and system based on NURBS local support, and the method is applied to a motion controller and comprises the steps: obtaining a control point sequence and a node information sequence of an NURBS curve, and extracting a local data sequence under a preset sliding window based on the control point sequence and the node information sequence; based on the local data sequence, obtaining a segmentation point sequence and a geometric feature sequence under a preset adaptive interval recursion mechanism; generating an expected speed set under a preset speed constraint condition based on the segment point sequence and the geometric feature sequence; carrying out binding processing on the basis of the geometric feature sequence and the expected speed set to obtain a structured trajectory data set; and performing interpolation processing on the motion controller based on the structured trajectory data set. According to the technical scheme provided by the invention, prospective interpolation can be realized based on the control points and node information of the NURBS curve, and the consumption of memory resources is reduced.

The invention relates to the technical field of industrial equipment internet of things systems, and discloses an edge computing laminating machine remote parameter debugging and fault diagnosis system which can trace process root causes from equipment abnormities or look forward equipment risks during process optimization by synchronously collecting equipment and process data and utilizing a causal network to perform bidirectional reasoning so as to improve the efficiency of process optimization. Therefore, a collaborative strategy of deep integration of the equipment and the process is generated, transformation from passive maintenance to active collaborative intervention is realized, and the comprehensive efficiency and reliability of a production system are greatly improved. The system can automatically adapt to changes of a production environment through a closed-loop self-learning mechanism based on feedback data, and long-term accuracy is kept. And in combination with cloud edge collaboration, group knowledge can be quickly fused, and deployment and popularization of intelligent applications are accelerated. The remarkable comprehensive benefits of reducing non-planned shutdown, improving product quality and equipment reliability, reducing operation and maintenance cost and expert dependence and supporting intelligent scale deployment are achieved.

Systems and methods are provided for generating a keyword sequence from an input query. A first text sequence corresponding to an input query may be received and encoded into a source sequence representation using an encoder of a machine learning model. A keyword sentence may then be generated from the source sequence representation using a decoder of the machine learning model. The decoder may generate a modified generation score for a plurality of prediction tokens, wherein the modified generation score is based on the respective prediction token generation score and a maximum generation score for a suffix of each prediction token. The decoder may then select the prediction token of the plurality of prediction tokens based on the modified generation score, and add the selected prediction token to the previously decoded partial hypothesis provided by the decoder.

A computer-implemented method, computer program product and computing system for: receiving speech in a source language to define source language speech; performing a first token-based transcription of the source language speech into text of the source language using a first look-ahead encoder to define source language text; and performing a first token-based translation of the source language speech into text of a target language using a second look-ahead encoder to define target language text, wherein the first look-ahead encoder is smaller than the second look-ahead encoder.

A method may include obtaining grid model data regarding a geological region of interest. The method may further include obtaining well data regarding a well in the geological region of interest. The method may further include obtaining a grid model for the geological region of interest based on the grid model data and the well data. The method may further include obtaining a time selection for a look-ahead simulation. The method may further include determining a look-ahead model for the look-ahead simulation based on the grid model data, the well data, and a coarsening function. The method may further include performing the look-ahead simulation using the look-ahead model and the time selection. The method may further include performing a reservoir simulation of the geological region of interest using the grid model and the look-ahead simulation.

Techniques are provided for reduced latency look-ahead for signal detection. An example methodology implementing the techniques according to an embodiment includes down converting a digitized signal to a first baseband signal at a first decimation rate such that the first baseband signal is provided at a first latency with a first signal to noise ratio (SNR) based on the first decimation rate. The method also includes down converting the digitized signal to a second baseband signal at a second decimation rate, greater than the first decimation rate, such that the second baseband signal is provided at a second latency with a second SNR based on the second decimation rate, the second latency greater than the first latency and the second SNR greater than the first SNR. The method continues with generating a detection threshold based on the first baseband signal prior to completion of the second baseband signal generation.

The application relates to the technical field of big model computing power scheduling, in particular to a computing power cooperative intelligent scheduling method and system based on a big model application, which comprises the following steps: obtaining a global computing power task request containing multiple big model inference tasks, extracting multi-dimensional features according to task constraint conditions and forming a task demand vector. An improved greedy algorithm is adopted to match the task demand vector and a state vector of a dynamic heterogeneous computing power resource pool, a preliminary scheduling instruction set is generated through weighted distance, a multi-step forward-looking evaluation model with computing power resource balance and task communication overhead as joint targets is constructed, the running state of tasks and resources in multiple time slices is simulated, the scheduling instruction is corrected, and finally the instruction is issued to a hardware computing unit to complete task execution. The method can improve the matching accuracy of tasks and computing power resources, optimize the balance of resource allocation, and reduce the data communication overhead between tasks.

The embodiment of the invention provides a power system look-ahead scheduling method and device, electronic equipment, a computer storage medium and a computer program product. The power system look-ahead scheduling method comprises the steps of obtaining a look-ahead scheduling model corresponding to a power transmission network in a power system; acquiring a response constraint model corresponding to the power distribution network in the power system; the response constraint model is obtained through training by taking the electric energy price as a sample and taking the corresponding response electric quantity as a label; fusing the response constraint model to the look-ahead scheduling model to obtain a fusion model; and solving the fusion model to obtain a look-ahead scheduling strategy. According to the embodiment of the invention, a more accurate and comprehensive look-ahead scheduling strategy considering the response characteristics of the power distribution network can be obtained. In addition, in the process of determining the scheduling strategy, the privacy data of the power distribution network user can be protected, and the data security is improved.

The invention provides a machining feedback control method and system based on high-purity hafnium wire drawing quality prediction, and relates to the technical field of machining control, and the method comprises the steps: collecting hafnium wire data in real time in a drawing process execution process, constructing a coupling feature tensor, inputting the coupling feature tensor into a multi-task depth fitting model based on physical constraints, and carrying out the multi-task depth fitting model; and performing quality evolution fitting in two quality dimensions of diameter precision and surface defect probability, generating a multi-scale quality evolution trend with a trust identifier, performing prospective compensation optimization under trust triggering, establishing a time sequence compensation strategy, performing strategy correction of the corresponding time sequence compensation strategy based on response time delay, and performing processing feedback control management. The technical problems that in the prior art, due to the fact that a drawing process is adjusted through empirical parameters, the quality fluctuation of hafnium wires is large, and the defect rate is high are solved. The technical effects of dynamically identifying and predictively controlling the quality state of the high-purity hafnium wire in the drawing process and improving the quality precision and the performance reliability of the hafnium wire are achieved.