137 results about "Online computation" patented technology

The invention relates to the technical field of electric power system regulation and control, and particularly discloses an electric power active balance control method for coupling multi-element energy storage of a coal power unit, which is used for solving the problem that the existing static active safety margin evaluation method cannot evaluate the safety of the coal power unit under the complex working conditions of high wind power penetration, large disturbance, system inertia decline and the like. The method solves the problems that multi-stage frequency response cannot be accurately described, online self-adaptive power distribution and continuous smooth scheduling are lacked, and consequently frequency drop is large, recovery is slow and output suddenly changes, and comprises the steps of building a collaborative characteristic model, calculating dominant characteristics on line, optimizing scheduling in a segmented mode and updating parameters in a closed loop. According to the method, by constructing a hierarchical inertia and multi-stage frequency modulation collaborative model, and adopting multi-scale online feature extraction and adaptive power distribution, segmented gradient optimization scheduling and closed-loop online parameter updating, quick response, smooth connection and accurate optimization of frequency support and economic scheduling in a wind power high-permeability scene are realized.

The invention relates to a fault-tolerant task reconstruction and scheduling optimization method for an unmanned aerial vehicle cluster, and relates to the technical field of unmanned aerial vehicle scheduling optimization, and the method comprises the steps: carrying out the rehearsal simulation based on preset cluster task planning in combination with a three-dimensional environment model of a task region, and generating a fault-tolerant plan library; issuing the fault-tolerant plan library to the target unmanned aerial vehicle cluster, executing cluster task planning, and continuously monitoring the global state of the cluster through a hierarchical distributed consensus mechanism; and performing node failure judgment according to the cluster global state, and triggering a fault-tolerant plan library to execute scheduling optimization of the target unmanned aerial vehicle cluster according to a node failure judgment result. According to the method, the problems that in task execution of a traditional unmanned aerial vehicle cluster, communication interruption is easily caused by node disability and obstacle interference, the online calculation amount is large, bandwidth occupation is high, fault-tolerant response delay occurs, and scheduling toughness is insufficient are solved.

The invention belongs to the technical field of equipment state monitoring, and particularly relates to a boiler wall temperature anomaly detection and early warning method based on approximate entropy calculation, which comprises the following steps: dividing a large coal-fired power plant boiler wall temperature time sequence into continuously overlapped sliding windows; calculating three statistical parameters in each sliding window; the three statistical parameters comprise standard deviation, range and approximate entropy; forming a three-dimensional time sequence matrix formed by n groups of parameters based on the three statistical parameters; and performing anomaly detection and early warning on the three-dimensional time sequence matrix by using an isolated forest anomaly detection algorithm. Abnormalities are accurately recognized from different dimensions, and high-dimensional data are efficiently dealt with. The method has the real-time performance and the dynamic performance, online calculation and real-time response can be achieved, the model is flexibly updated along with new data inrush, and the anomaly detection requirement of the dynamic data environment is met.

Quantizing tensors and vectors processed within a neural network reduces power consumption and may accelerate processing. Quantization reduces the number of bits used to represent a value, where decreasing the number of bits used can decrease the accuracy of computations that use the value. Ideally, quantization is performed without reducing accuracy. Quantization-aware training (QAT) is performed by dynamically quantizing tensors (weights and activations) using optimal clipping scalars. “Optimal” in that the mean squared error (MSE) of the quantized operation is minimized and the clipping scalars define the degree or amount of quantization for various tensors of the operation. Conventional techniques that quantize tensors during training suffer from high amounts of noise (error). Other techniques compute the clipping scalars offline through a brute force search to provide high accuracy. In contrast, the optimal clipping scalars can be computed online and provide the same accuracy as the clipping scalars computed offline.

The invention discloses a multi-modal KV cache retrieval method and system based on a hybrid architecture, relates to the technical field of data processing, and aims to solve the technical problem that in the prior art, due to the adoption of a full-online computing architecture and lack of a unified cross-modal representation framework, the computing efficiency and the modal fusion effect are difficult to consider at the same time. By constructing a hybrid processing architecture of offline pre-calculation and online processing, multi-modal data is subjected to unified fragmentation coding and KV cache and semantic fingerprints of the multi-modal data are pre-calculated in an offline stage, and cache fragments are intelligently assembled and position codes are dynamically remapped based on a user query intention in an online stage; therefore, online repeated calculation of multi-modal content is fundamentally avoided, coherent alignment and efficient fusion of cross-modal semantics are achieved, the real-time response capability, the cache reuse rate and the multi-modal generation quality of the system are remarkably improved, and high-concurrency scenes can be dynamically adapted.

The invention relates to a reusable rocket landing section fuel optimal conversion time sequence online generation method and device and a medium, and the method mainly comprises the steps: simplifying the complex three-dimensional motion of a rocket vertical landing section into a one-dimensional vertical motion model; establishing a nonlinear equation set which takes the working time of the engine before and after stage conversion as an unknown quantity and enables the rocket to meet the terminal speed and terminal position constraints and the fuel consumption to be optimal; constructing and solving an approximate analytical solution as an initial value of Newton iteration; and based on the obtained initial value, carrying out iterative solution on a double-constraint nonlinear equation set formed by rocket speed and displacement terminal constraint. According to the method, a form based on analytic iteration is adopted, the fuel optimal control in the rocket landing process is realized through real-time online calculation of an engine conversion time sequence and giving of a corresponding thrust adjustment control instruction, and safe and accurate landing is ensured.

The invention discloses an unmanned aerial vehicle wall-attached disturbance suppression method based on strategy optimization, and aims to solve the problems that in the wall-attached flight or operation process of an unmanned aerial vehicle, strategy optimization needs to be updated online, strong constraints such as the minimum safety distance, the attitude angle, the speed and the acceleration are easily violated, airborne computing power is limited, and safety and real-time performance are difficult to consider at the same time. According to the invention, reachability analysis is carried out based on a dynamical model and wall geometry to train a feasible region approximate network so as to construct a safety supervisor, an initial control instruction is generated by adopting a hierarchical strategy, and optimization and correction are carried out under the constraint of a feasible control region to obtain a safety control instruction. Performing event-triggered online updating based on the intervention amount of the safety supervisor, introducing supervision constraint learning which takes a safety control instruction as a supervision target, and replacing a baseline strategy when a candidate strategy meets a switching condition; the technical effects of improving the adherence disturbance suppression capability and reducing the on-line calculation burden on the premise of meeting the safety constraint are achieved.

A system for speech-to-text conversion of passenger announcements on board of an aircraft comprises a cabin management control configured to provide a speech signal related to an announcement to passengers on board of the aircraft; a cabin application server comprising a processing unit configured to convert the speech signal into a text message containing text corresponding to spoken words of the speech signal, wherein the processing unit is configured to convert the speech signal locally without accessing online computing resources; and a network interface configured to provide the text message to at least one passenger device on board of the aircraft.

The invention belongs to the technical field of transformer winding temperature field rapid calculation, and discloses a transformer winding heat flow field calculation method and a related device, working condition data of a transformer winding are obtained, and the working condition data comprise winding current, winding inlet flow velocity and inlet temperature; the working condition data are processed through a trained neural network model, corresponding key physical quantity information of the transformer winding under the working condition data is obtained, and the key physical quantity information comprises temperature distribution of the transformer winding, an oil flow velocity radial component, an oil flow velocity axial component and oil flow internal pressure. The method can meet the requirement for real scene online calculation of the temperature field of the power equipment of the transformer substation, has important application value for state early warning and reliability evaluation of the transformer, and solves the problems that current mainstream multi-physics field numerical calculation software is low in calculation efficiency and difficult to provide technical support for multi-physics field online real scene display of the power equipment.

The invention belongs to the technical field of microstructure and multi-scale structure design, and particularly relates to a microstructure rapid design and filling method and system based on macroscopic strain. Constructing a microstructure topological optimization model based on the macroscopic strain, sampling the macroscopic strain and the volume fraction to generate a corresponding optimized microstructure, and establishing a microstructure training data sample library with labels; carrying out equivalence analysis on a strain energy expression, solving the problem of variable inconsistency by utilizing an intermediate variable, and converting the strain in the training sample into the intermediate variable; establishing a deep neural network with an intermediate variable and a volume fraction as input and a microstructure topological configuration as output, and training the deep neural network by using a data sample to obtain a microstructure fast reverse design network; and carrying out staged multi-scale structure filling design by using the microstructure fast reverse design network. According to the method, the online calculation cost of microstructure and multi-scale structure design can be reduced, and the design efficiency is improved.

The present invention discloses a method for online identification of the dynamic parameters of an articulated serial robot, comprising the following steps: S1. For the articulated serial industrial robot to be identified, a robot dynamic model that takes friction into account is established; S2. The dynamic model of the articulated serial industrial robot is linearized; S3. An optimal excitation trajectory is designed for the identification experiment, using a five-term finite Fourier series to obtain the excitation trajectory by solving a constrained nonlinear optimization problem; S4. The serial robot runs the excitation trajectory while simultaneously collecting the position information of each joint of the robot and the driving current of each joint at each moment, and online calculating the parameters to be identified at each moment. The present invention can perform online identification of robot dynamic parameters, improves the efficiency of robot dynamic parameter identification, and can be used in industrial scenarios such as robot mobile handling, polishing and grinding, and heavy-load hydraulics.

The application discloses a hand rehabilitation training system and adaptive training method based on brain-muscle coupling and force feedback, comprising a force feedback exoskeleton device, a training task interaction module, an electroencephalogram-electromyogram acquisition module, a synchronous control module, a signal preprocessing and feature extraction module, a brain-muscle coupling analysis module, an adaptive training adjustment module, and a safety constraint and degradation control module. The system is based on a virtual environment, and the user wears the force feedback exoskeleton device to complete the grasping task, and the electroencephalogram and electromyogram signals are synchronously acquired, the brain-muscle coupling indexes of the trial and round are calculated, and the training task difficulty parameters and the force feedback intensity parameters are updated based on the indexes, and the parameter updating process is safety constrained, and the degradation control is executed in abnormal conditions. The application realizes the closed-loop adaptive adjustment of the hand rehabilitation training task difficulty and the force feedback intensity with controllable online calculation, and is helpful to improve the training adaptability and the central-peripheral collaborative training effect.

The invention discloses a judgment feedback method for the selective ore grinding dissociation effect of a stirring mill and a computer system.The judgment feedback method comprises the following steps that S1, a comprehensive evaluation mathematical model Mb of the selective ore grinding dissociation effect is constructed; s2, determining a threshold value T of the mathematical model Mb according to historical field production data or tests; when the Mb value calculated in real time is lower than a threshold value T, the system judges that the current selective ore grinding dissociation effect is poor, and a regulation and control instruction is automatically triggered; and when the Mb value calculated in real time is continuously higher than the threshold value T and is kept stable, the system judges that the current ore grinding effect is good and excessive energy is input, and an energy-saving regulation and control instruction is triggered. According to the method, the comprehensive evaluation value of the ore grinding effect is calculated on line by establishing a comprehensive mathematical model formula Mb, a threshold triggering feedback mechanism is set, key operation parameters of the mill are automatically adjusted, and closed-loop control of evaluation-feedback-regulation is formed.

The invention discloses an anti-parameter disturbance bidirectional converter non-linear control method and system, and the method comprises the following steps: building a three-order non-linear dynamic model of a three-phase voltage source converter, defining the reciprocal 1 / Rc of an AC side resistor R and a DC side load resistor in the non-linear dynamic model as an unknown parameter, and calculating the parameter disturbance of the three-order non-linear dynamic model; obtaining a system dynamic equation with parameter uncertainty of the three-phase voltage source converter; designing a state transformation function, linearizing a system dynamic equation, and mapping a state variable x; s30, a tracking error variable is defined as a difference value between the converted state variable and a reference track of the state variable, and the difference value is used for quantifying the deviation between the actual state and the expected state of the system; defining and designing a parameter updating law of the three estimated values, and calculating and updating unknown parameters on line according to the tracking error variable; and designing and calculating a control input law based on the tracking error variable and the three estimated values, and proving the asymptotic stability of the closed-loop system through a Lyapunov function.

The invention discloses a multi-satellite large-scale MIMO (Multiple Input Multiple Output) downlink precoding method and a multi-satellite large-scale MIMO downlink precoding system considering power amplifier nonlinearity. According to the method, different time delays and Doppler frequency shifts of different satellite signals arriving at a user terminal are considered, and pre-compensation is carried out on each satellite sending signal; the input and output characteristics of the satellite side nonlinear power amplifier are modeled by using the polynomial model; establishing an optimization problem to jointly design a precoding vector and a receiving vector; the signal interference noise distortion ratio in the optimization problem considers interference and distortion from other satellites, interference and distortion in the satellite and a noise item; and converting a sum rate maximization problem into a weighted mean square error minimization problem for solving. And a traditional iteration updating step is further mapped into a learnable network layer structure through a deep expansion algorithm, so that the calculation complexity is reduced. Compared with the traditional method, the method provided by the invention is more in line with the actual transmission condition, can improve the system performance and reduce the online calculation overhead, and is suitable for the precoding design in a multi-satellite communication scene.

The invention discloses an online calculation method and analysis system for coupling impedance between multi-feed direct current systems, and belongs to the technical field of high-voltage direct current transmission control. The online calculation method of the coupling impedance is provided for solving the problems that the coupling impedance of a multi-feed direct current system under large-scale new energy access is complex and changeable, and off-line calculation is difficult, and the method further comprises the steps that an equivalent model of the multi-feed high-voltage direct current power transmission system is constructed; on the basis of the equivalent model, the coupling impedance can be calculated in real time to use the electrical quantity in a steady state. The method not only can be used for researching the interactive coupling characteristic in a large power grid, but also can be used for analyzing the risk of an anticipated accident so as to cope with a complex and changeable operation mode of new energy.

The present invention discloses a method and system for online calculation of turbine guide vane leakage, relating to the technical field of operation optimization and maintenance of hydroelectric generator sets. The method comprises collecting historical data of the unit's shutdown process; establishing a relationship between the unit's angular velocity change rate and hydraulic torque to output the unit's guide vane leakage; constructing a database of relationships between head, leakage, and deceleration duration based on the leakage effect; and outputting the unit's leakage using the head value. The method for online calculation of turbine guide vane leakage provided by the present invention outputs the unit's guide vane leakage by establishing a relationship between the unit's angular velocity change rate and hydraulic torque, thereby improving the accuracy of turbine performance analysis and the ease of operation, enhancing the monitoring and diagnosis capabilities of the unit's operating status, and increasing the unit's operating efficiency and reliability. The present invention achieves improved results in terms of the accuracy, reliability, and monitoring efficiency of leakage monitoring and prediction.

This invention discloses a monitoring method and system for the preparation process of ternary cathode materials based on RVAE. It constructs a nonlinear dynamic system model of the sintering process based on a variational autoencoder; assigns different weights to samples at different times in the constructed nonlinear dynamic system model of the sintering process, derives the loss function of the nonlinear dynamic system model of the sintering process, and trains the model parameters through backpropagation; defines the statistics of the nonlinear dynamic system model of the sintering process based on the cyclic variational autoencoder, and obtains the control threshold of the nonlinear dynamic system model of the sintering process through kernel density estimation; collects online data as a test set for the nonlinear dynamic system model, calculates the monitoring statistics online and compares them with the control limits to determine whether a fault has occurred. This invention can significantly improve the fault detection rate and false alarm rate, providing a strong guarantee for the stable operation of the sintering process.

The invention discloses an online optimized coffee bean blending method and system and a coffee machine. The method comprises the following steps: a) acquiring a training data set containing coffee bean type information, baking curve parameters, a blending proportion and corresponding coffee sample flavor index quantitative data; b) training a machine learning model based on the training data set, and establishing a mapping relation between the input parameters and flavor indexes; c) receiving a target flavor index input by a user; and d) performing online calculation by utilizing the trained model and an optimization algorithm according to the target flavor index to obtain an optimal or suboptimal coffee bean blending proportion and baking curve parameter combination. According to the method, a personalized coffee formula scheme can be quickly and accurately provided according to user requirements, the research and development efficiency and the product consistency are remarkably improved, and continuous optimization can be realized through user feedback. The method can be realized through a system comprising devices for data acquisition, model training, user interface, optimization calculation and the like.

The application discloses a large language model inference service key path perception scheduling method for multi-agent workflow, wherein a lightweight DAG registration center is embedded in the inference engine service layer, a standard REST interface is used to accept the task DAG registered by a client, the residual cost (cp_remaining) of each subtask node is calculated online, the scheduling priority of the inference request is converted, the priority queue built in the inference engine is driven to preferentially process the inference request on the key path, the expected cost of a random subtask with uncertain execution rounds is calculated based on a geometric distribution model, and an online cost updating mechanism based on the self-estimation of the large language model is proposed for the subtask with an undetermined cost, so that the online self-adaptation of the scheduling priority is realized through the dynamic updating of the node cost. The application keeps the backward compatibility for the ordinary inference request without carrying the DAG metadata, and can be deployed without modifying the core scheduling logic of the inference engine.

The invention discloses a method for calculating load influence on line and related equipment, and belongs to the field of software algorithms, and the method comprises the following steps: if an instruction for calculating the load influence is received, receiving and processing load data, and carrying out inflection point judgment on the processed load data; if judging that inflection point data exists in the processed load data, extracting the inflection point data, and storing the inflection point data into a data buffer; determining a first inflection point data set selected by the pointer; and extracting the first inflection point data set, and carrying out load influence calculation on inflection point data in the first inflection point data set through a preset load influence calculation algorithm. According to the method, the calculation amount of each iteration is controlled by introducing the pointer, so that the calculation amount of each iteration can be predicted, and the algorithm can be applied online in real time.

The application discloses a hybrid off-road vehicle energy management method, device, medium and product, and relates to the field of vehicle energy management. The application classifies off-road working conditions based on test data by using a K-means clustering algorithm and relative entropy to obtain a classification result, simplifies variable off-road environments into a transfer probability matrix of electric power demand under different working conditions, greatly reduces the complexity of off-road working condition classification, and realizes an energy management strategy which is adaptively changed in real time according to a driving working condition. Moreover, the application determines an optimal solution corresponding to each type of working condition by using an improved expected SARSA algorithm based on the classification result, reduces online calculation time, and further solves the problems that an energy management strategy based on traditional reinforcement learning is slow in training speed and difficult to realize real-time control of a vehicle.

The invention relates to an online calculation system, method and equipment for a steel pipe rolling tension reducing mill and a medium, and belongs to the technical field of digitization. The method comprises the following steps: inputting production plan information through a superior system or in a manual mode; importing or manually inputting parameters through a template to generate hole pattern data; after a user selects a plan, a system automatically matches corresponding hole pattern data; a user configures and adjusts CEC control strategy parameters and then triggers a calculation process, and the system returns rolling and CEC related result data; the user can choose to store the calculation result in a database for subsequent production reuse or lower-level system call. Hole pattern data can be intelligently matched, and it is ensured that the hole pattern data is highly matched with a current production plan; a hole pattern scheme is flexibly generated, rapid and efficient calculation is achieved, and the operation efficiency is remarkably improved; and historical data storage and reuse functions are provided, the production process is optimized, and repeated input and calculation work is reduced. The whole system effectively improves the production efficiency, the system flexibility and the user interaction experience.

Disclosed is a real-time online public access computer implemented system for calculating algorithms for valuing an alternative asset based on correlative technical performance indicators of relative peer assets.

In a method and system for viewer interaction with streaming media, a broadcast device including an operating system level broadcaster interface overlay is utilized to display a scene from a media presentation. The overlay generates image frames from the scene of the media presentation. The image frames are transmitted to a viewer device. Viewer interaction parameters are received from the viewer device and a viewer interaction is displayed on a subsequent scene of the media presentation utilizing the broadcaster interface overlay. On the viewer device, image frames of the media presentation are received from the broadcast device. An operating system level viewer interface overlay is generated on the image frames and the image frames of the media presentation are displayed. Viewer interaction parameters are generated from viewer interaction with the operating system level viewer interface overlay and transmitted to the broadcast device.

The present disclosure relates to the technical field of power system operation management, and discloses a power distribution network line loss online calculation method, comprising the following steps: performing power flow calculation by using power distribution network historical data, establishing a training set and a test set based on the calculation result; constructing a power distribution network line loss calculation model, and performing offline training on the online calculation model using the training set; testing the calculation model training effect by using the test set and outputting an online application model to calculate the power distribution network line loss. The present disclosure can accurately and timely calculate the power distribution network line loss.

The application discloses a kind of based on expert statistical feature incremental calculation updating method, electronic equipment, medium, the method of the present application is divided into offline calculation and online calculation, wherein offline calculation first reads the configuration information of expert statistical feature;Then extract the user statistical association information of the statistical feature of historical data;Finally, the statistical association information of historical data is persisted.Online calculation first reads the configuration information of expert statistical feature;Then extract the user statistical association information of the statistical feature of new data;While reading the user statistical association information persisted by historical data;Next, the statistical association information generated based on historical and new data extraction is updated offline data statistical association information and executes persistence operation;Finally, the corresponding statistical feature is generated based on the statistical association information of updated offline data.The method of the present application can still efficiently output statistical features for low server resources.

1. Name of the product in this design: Electronic device with a graphical user interface for online calculation and display of agricultural algorithms. 2. Purpose of this design: To operate and display the image data results of agricultural algorithm calculations. 3. The key design features of this product are the content and layout of the user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: The main view is the user interface for online calculation of agricultural algorithms. The top is the navigation bar, and the left side is the algorithm list and data list. The button at the top left can switch between the agricultural algorithm list and the data list. The upper right corner displays basic data information. The bottom right side is the input interaction box, where you can select the input image and parameters, and click OK to perform the calculation. The middle section displays the algorithm's computational results in an image.

The application discloses a DCO-OFDM peak-to-average ratio suppression method and system based on multi-stage transfer learning, generates a candidate symbol set through constellation extension; the candidate symbol set is preprocessed into a feature tensor and input into a hybrid deep decision network composed of a local feature extraction module, a global information interaction module and a decision output module in cascade; the network outputs a probabilistic decision weight, and through a fully differentiable channel from the decision weight to PAPR calculation, a composite objective function containing a PAPR performance loss term and a decision entropy regularization term is designed, and a multi-stage progressive transfer learning strategy is adopted for end-to-end unsupervised training. Through the special network architecture and training strategy, the application effectively reduces the PAPR of the DCO-OFDM system, reduces the nonlinear distortion, simultaneously transfers the complex search process to the offline training stage, reduces the online calculation complexity, and improves the convergence speed and the final performance of the model.

A computer-implemented method for predicting coal quality of coal mill based on neural network is provided. The method includes: establishing a prediction model based on a neural network structure; importing cleaned coal mill data for model training; connecting the trained prediction model with a distributed control system (DCS) in real time; calculate online the running coal type of the coal mill; and determining the real-time running coal quality of the coal mill according to the calculation results.