80 results about "Look-ahead" patented technology

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 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 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.

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 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 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.

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.

A sequence of compressed video frames is received. A transformer diffusion model is applied to the sequence of compressed video frames. Applying the transformer diffusion model includes utilizing a look around model in an encoding portion of the transformer diffusion model and a look ahead model in a decoding portion of the transformer diffusion model. A restored video sequence is generated based on an output of the transformer diffusion model

This invention relates to a method for achieving toolpath corner transition processing in laser cutting beveling operations, comprising the following steps: drawing a toolpath with an angle; obtaining the jet direction at the turning point; importing the structural parameters of the laser head and the generation parameters of the toolpath algorithm; adjusting the jet direction at the corner; calculating the look-ahead distance before and after the turning point; and generating the toolpath. This invention also relates to a device, processor, and computer-readable storage medium for achieving laser cutting beveling corner transition. By employing the method, device, processor, and computer-readable storage medium of this invention for achieving toolpath corner transition processing in laser cutting beveling operations, continuous cutting at beveling corners is achieved, improving cutting efficiency. This invention avoids the problem of machining failure due to excessive angle at the corner.

Apparatuses, systems, and techniques related to a dual lookahead pass for video encoding. In at least one embodiment, frames of the video are processed during a first lookahead pass to extract characteristics and determine a fixed group of pictures (GOP) structure, the fixed GOP structure is then optimized based on the extracted characteristics to generate an adaptive GOP structure that is processed during a second lookahead pass.

This invention proposes a control method, system, and CNC machine tool for follower rollers. The method includes: acquiring reference information of support points corresponding to each angle during one revolution of the pipe; acquiring the current angle information of the feeding rotation axis during the current processing; querying the support point reference information based on the current angle information to determine the basic support position corresponding to the current angle; acquiring the physical parameter information of the pipe and establishing a flexural deformation model of the pipe; calculating the additional compensation amount at each roller position based on the model and the axial installation position of each follower roller; determining the final target position of each follower roller based on the basic support position and the additional compensation amount; and then controlling each follower roller to move synchronously to its respective final target position. Based on this method, a corresponding system and CNC machine tool are also proposed. This invention integrates static theoretical data with dynamic self-learning calibration, and combines multi-axis collaborative compensation and look-ahead predictive control to achieve precise adaptive support for the pipe.

The invention relates to a bridge structure toughness state regulation and early warning method and system, and solves the problems of splitting and response passive lag of each link in bridge operation and maintenance, and the method comprises the steps: collecting the internal sensing data of the bridge structure state and material performance, synchronously obtaining the external multi-dimensional data of weather, traffic and geology, and carrying out the early warning of the bridge structure toughness state. A global sensing dynamic information field is generated through fusion processing; based on the information field, driving the digital twin to carry out multi-scene coupling deduction, and when a response exceeds a threshold value, deciding and outputting a graded early warning signal and a corresponding pre-intervention strategy according to a toughness evaluation system; according to the early warning signal and the strategy, if an automatic execution condition is satisfied, a control instruction is issued to the distributed network to execute active adjustment; otherwise, generating a digital plan and pushing the plan to a specified terminal. The method has the following effects that fundamental transformation of bridge operation and maintenance from passive lag to active look-ahead is achieved, and the self-adaptive capacity and the overall toughness of the system are remarkably enhanced through intelligent closed-loop linkage.

This invention discloses a prediction method and system for over- and under-excavation data using artificial intelligence analysis, specifically relating to the fields of over- and under-excavation prediction and edge data transmission. The method includes collecting cross-section point data, attitude data, and working condition data corresponding to the current tunneling cycle; dividing the cross-section point data into multiple cross-section segments according to the same advance interval; and mapping the attitude data and working condition data to each cross-section segment according to time sequence, outputting a data set indexed by the cross-section segments. Under the condition of limited edge data transmission, the method performs censoring and reconstruction verification on the data within the cross-section segments to identify irretrievable early anomaly evidence. The identified early anomaly evidence is then organized into evidence segments according to the continuity of position and time, and combined with contour, attitude, and working condition information for time-series prediction analysis. This addresses the problem of easily lost initial anomaly evidence of over- and under-excavation under the condition of limited edge data transmission, leading to insufficient basis for forward-looking prediction.

It's about choosing your actions effectively. Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an action from a set of actions to be performed by an agent interacting with an environment to cause the agent to perform a task. One of the methods includes receiving current observations characterizing a current environmental state of the environment, and selecting an action to be performed by the agent in response to the current observations by performing multiple iterations of an external lookahead search, where performing the multiple iterations of the external lookahead search includes, in each external lookahead search iteration, determining a proper subset of possible future states of the environment, determining that one or more internal lookahead search initiation criteria are satisfied, and in response thereto, performing an internal lookahead search of the proper subset of the possible future states of the environment.

The invention discloses a data quality control method and system based on quality function expansion, and the method comprises the steps: carrying out semantic recognition to obtain demand direction features and specific demand features, and carrying out the matching of different demand modules according to the demand direction features, calculating a demand-process correlation degree, constructing an incidence matrix, determining first process parameters of different demand modules, carrying out conflict detection on the first process parameters, calculating a look-ahead dynamic weight, carrying out internal correction to obtain second process parameters, carrying out external correction to obtain third process parameters, and calculating demand weights, fusing the third process parameters to obtain quality control process parameters, calculating demand weights in different demand directions, and fusing the third process parameters in different demand directions according to the demand weights to obtain the quality control process parameters. The method not only can improve the efficiency and accuracy of data quality control, but also has good interpretability, and can be directly applied to a data quality control system.

A non-transitory machine-readable medium having data stored therein representing a software executable by a computer. The software executable comprising instructions configured to receive a first data set of one or more electromagnetic measurements at a first location in a borehole from a logging tool, receive a second data set of one or more electromagnetic measurements at a second location in the borehole from the logging tool, and perform a first inversion on the first data set of one or more electromagnetic measurements to form a first inverted data set. The software executable further configured to perform a second inversion on the second data set of one or more electromagnetic measurements to form a second inverted data set, compare the first inverted data set to the second inverted data set to identify a gradient change between each of the one or more electromagnetic measurements in the first inverted data set and the second inverted data set, and alter course of rotary steerable system (RSS) based at least in part on the gradient change.

Dual lookahead passes for video encoding are disclosed, specifically apparatuses, systems, and techniques related to dual lookahead passes for video encoding are disclosed. In at least one embodiment, frames of a video are processed during a first lookahead pass to extract features and determine a fixed group of pictures (GOP) structure, which is then optimized based on the extracted features to generate an adaptive GOP structure that is processed during a second lookahead pass.

This invention discloses a collaborative path planning method based on multi-constraint improved A* algorithm and a dynamic look-ahead mechanism. It constructs a feature distance field map containing a nonlinear safety potential field, transforming discrete environmental information into continuous safety gradients. An improved A* algorithm, incorporating dynamic adaptive heuristic weights, nonlinear safety potential field costs, and kinematic smoothness constraints, is used to plan the globally optimal path. A velocity-adaptive dynamic look-ahead mechanism is established to calculate dynamic look-ahead points that conform to the current motion state in real time. A multi-objective evaluation function containing a global traction index is introduced into the local planning process to optimize control commands by measuring the deviation between the local trajectory and the global intent. This invention effectively solves the problems of "wall-hugging effect," high computational cost in large-scale environments, and poor coordination between global and local planning in traditional methods, improving the navigation safety and smoothness of robots in complex unstructured scenarios.

The application provides a semiconductor equipment disturbance prediction and control method, system, device and medium, the method comprising: first, presetting the mapping rule between the process instruction and the disturbance event, analyzing the process instruction in the process recipe during the operation of the semiconductor equipment; screening the disturbance instruction based on the look-ahead window mechanism, converting the disturbance instruction into the disturbance event, and generating the corresponding disturbance prediction curve; generating a plurality of feedforward compensation components based on the disturbance prediction curve, superimposing the feedforward compensation components, obtaining the comprehensive feedforward compensation control quantity, mapping each comprehensive feedforward compensation control quantity to the prediction time axis, and obtaining the feedforward compensation curve; calculating the temperature error based on the current temperature value and the target temperature of the temperature control system, and obtaining the feedback control quantity combined with the PID algorithm; superimposing the comprehensive feedforward compensation control quantity and the feedback control quantity to obtain the final control output quantity, and sending the final control output quantity to the temperature control system to drive the heating or cooling device.

Cyclic redundancy check (CRC) algorithms are utilized in digital communication and storage systems for error detection. CRC encoding and decoding may be implemented using linear feedback shift registers (LFSRs). To achieve high throughput, a parallel LFSR may be implemented by registers using feedback matrix and pre-processing matrix multiplication. In conventional architectures, a feedback matrix may be decided by look-ahead computations of an FSR, and the corresponding matrix multiplication may contribute to a significant portion of the overall complexity of the LFSR. The methods described herein are provide for searching over a wide range of powers of a companion matrix describing the parallel LFSR in order to minimize the logic gate count required for the feedback matrix multiplication.