19597results about "Programme control" patented technology

The invention relates to the technical field of body-equipped intelligent robots, in particular to a multi-mode body-equipped intelligent robot control method and device, and the method comprises the steps: synchronously collecting visual, auditory, tactile, force sense and body perception information, and unifying the information to the same time-space reference through a cross-mode time-space stamp alignment mechanism; hierarchical feature extraction and fusion are carried out on the multi-modal information, and unified multi-modal scene state representation is generated; reasoning a decision based on the representation by using a body agent framework, and outputting a control instruction; motion planning and control, visual servo tracking in a non-contact stage and dynamic parameter correction in a contact stage are executed according to instructions; optimizing the multi-modal strategy network through an incremental strategy distillation mechanism based on the interactive data flow; the problem of space-time asynchronization of multi-modal sensing information is solved through a cross-modal space-time stamp alignment mechanism.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention discloses a precision compensation method of a composite numerical control machine tool, and particularly relates to the technical field of precision control of the numerical control machine tool, which comprises the following steps: synchronously acquiring temperature, vibration and force information under the driving of a unified clock through multiple types of sensors deployed at key nodes of the machine tool, and generating a synchronous multi-source sensing data set with aligned timestamps; inputting the data set into a multivariable coupling error model obtained through data driving training, and calculating and generating a comprehensive space volume error predicted value of a tool nose point of the tool; performing inverse calculation based on a machine tool kinematic chain model, and generating a multi-dimensional micro-compensation instruction set containing the compensation amount of each motion axis and the sequential relationship; and finally, dynamically writing the instruction set into a servo control ring of the numerical control system through a real-time data interface, and driving each motion shaft to execute synchronous compensation motion. According to the method, comprehensive compensation of multi-physics field coupling errors such as temperature, vibration and force load is achieved, and the machining precision and stability of the numerical control machine tool under complex working conditions are improved.

The invention discloses a numerical control machine tool wear monitoring and intelligent compensation control method based on deep learning, and relates to the field of numerical control machine tool wear monitoring. Multi-source data are collected through a sensor, and are preprocessed and fused through edge calculation; deep features are extracted and enhanced through an improved network, the abrasion state is evaluated through a mixed expert model, and life is predicted in combination with survival analysis; based on enhanced transfer learning, generating an intelligent compensation strategy according to a processing target, and executing the intelligent compensation strategy after verification in a virtual environment; a closed-loop system is constructed, all modules are acquired, fed back and optimized, and full-process intelligent management of functions such as knowledge graph early warning and multi-machine-tool cooperation is integrated. According to the method, the wear monitoring accuracy is improved, and early wear is accurately recognized; machining parameters are intelligently compensated and optimized, precision is improved, and the service life of a tool is prolonged; closed-loop control and multiple technologies are fused, the response time is shortened, and shutdown is reduced; the operation efficiency and reliability of the numerical control machine tool are improved, and the cost is reduced.

The invention discloses a building robot multi-machine collaborative operation optimizing and monitoring method and system based on digital twinning, and belongs to the technical field of building construction intellectualization. The method comprises the following steps: constructing a digital twinborn body comprising an environment perception model and a kinematics / dynamics model based on a building information model and robot physical parameters; performing multi-machine task allocation and path planning containing static / dynamic obstacle avoidance in the virtual environment; synchronizing physical environment data in real time through a multi-mode sensor; dynamically optimizing operation parameters by adopting a genetic algorithm or a particle swarm algorithm and realizing closed-loop control; and generating a safety early warning and emergency scheme based on machine learning. According to the method, Markov decision path planning of reinforcement learning and multi-agent game task allocation are creatively fused, laser radar-vision-inertial navigation multi-source data fusion is adopted, the technical problems that in a traditional method, digital twinning precision is insufficient, and dynamic cooperation efficiency is low are solved, and the method is suitable for large-scale popularization and application. And the construction efficiency, the safety and the man-machine interaction experience are remarkably improved.

The invention provides a numerical control machine tool dynamic precision control method and system based on multi-axis linkage error compensation, and relates to the technical field of mechanical manufacturing, and the method comprises the steps: collecting shaft system motion parameters and real-time cutting load parameters of a multi-axis numerical control machine tool in a linkage machining process in real time; shafting motion parameters comprise displacement, speed and acceleration of each feed shaft, and cutting load parameters are obtained through inversion of main shaft current and servo driving torque; calculating a dynamic following error of each feed shaft in a linkage state based on the shaft system motion parameters; and carrying out load coupling correction on the dynamic following error by fusing real-time cutting load parameters to obtain an initial multi-axis linkage dynamic error. According to the invention, real-time precision control in the high-dynamic machining process can be realized.

The invention discloses a flowmeter-oriented embedded multi-parameter fusion calibration control method and system, relates to the technical field of industrial measurement and embedded control, and is used for solving the problem of unstable calibration caused by misalignment of multi-link flow signals, compensation lag and fusion oscillation. The method relates to synchronous acquisition, pretreatment and calibration of differential pressure type, ultrasonic wave and thermal type mass measurement links and environment parameters such as temperature, pressure and density. All-channel unified time scales are established through ternary binding, filtering and denoising, zero snapshot and alignment rules, a stability scoring mechanism is constructed in combination with offset behavior recognition, event classification and feature aggregation, and dynamic switching of a compensation path and a fusion strategy is driven. In a double-path fusion structure, table look-up compensation, model estimation, multi-algorithm parallelism and fusion track recording are adopted, version traceability and calibration result closed-loop control are achieved, and the stability and rechecking performance of mass flow output under complex disturbance are effectively improved.

The invention relates to the technical field of industrial control, in particular to an intelligent centerless grinding machine cooling system and method for difficult-to-machine materials, and the system comprises a multi-source sensing unit, an edge calculation module, a decision center module, a precise execution module and a digital immune memory bank; compared with the defects that in the prior art, monitoring depends on a single temperature sensor, thermal field sensing is incomplete, thermal damage early warning lags behind and the like, according to the scheme, grinding power frequency spectrum, acoustic emission signals, triaxial vibration and coolant mass spectrum data are collected in real time through a multi-source sensing unit, and a micron-sized temperature field is reconstructed in combination with an edge end space-time diagram convolutional network; full-dimensional monitoring of the grinding thermal-mechanical coupling effect is achieved, and thermal anomaly recognition sensitivity and early warning timeliness are remarkably improved.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a control system configured to adjust the variable lighting assembly. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.

A controller configured to control a sound producing module includes a volume controlling unit configured to determine a demodulation amplitude and a modulation amplitude corresponding to a target volume. The sound producing module comprises a driving circuit and an air-pulse generating device. The driving circuit generates a demodulation driving signal according to the demodulation amplitude and generates a modulation driving signal according to the modulation amplitude, so as to drive the air-pulse generating device. The air-pulse generating device produces sound via generating a plurality of air pulses at an ultrasonic pulse rate.

The invention relates to the technical field of process control, and particularly discloses an unmanned aerial vehicle real-time control system and method based on 5G network and perception fusion. The system comprises an airborne sensing and communication module, an edge collaborative decision center and a global resource management and task planning platform, and dynamic collaboration of sensing data fusion, real-time decision and resource scheduling is realized through a multi-layer closed-loop control framework, so that the trajectory tracking precision and the system response agility of an unmanned aerial vehicle group are improved, and the system performance is improved. And the utilization efficiency of limited wireless resources is optimized.

The invention relates to the technical field of pulsed ion beam processing, and discloses a positioning system for a pulsed ion beam processing optical element, which can accurately predict nonlinear structure deformation in a complex thermal environment and eliminate compensation deviation caused by a traditional linear model. A multi-sensor data fusion mechanism effectively suppresses the interference of local measurement noise on a control decision, and improves the reliability of a compensation strategy. And a closed-loop control system realizes real-time verification and dynamic optimization of the compensation effect, and machining precision reduction caused by error accumulation is avoided. The submicron executing mechanism ensures that the thermal drift compensation amount is accurately converted into platform pose adjustment, the high-precision optical element machining requirement is met, and the problem that a traditional positioning system is incomplete in model input due to the single temperature field information collection dimension is solved. Meanwhile, the dynamic characteristics of the temperature-displacement coupling relation are verified through high-precision displacement data.

The invention relates to a dynamic precision compensation control method based on an intelligent PCB drilling machine, and the method comprises the steps: collecting a dynamic parameter set in real time through a multi-source sensor group disposed on a drilling main shaft, the dynamic parameter set comprises displacement deviation caused by axial deformation of a main shaft, thermal deformation analyzed based on interlayer temperature gradient and frequency spectrum characteristics reflecting tool-material coupling vibration, and according to the multi-dimensional compensation vector and current drilling process parameters, a space trajectory correction instruction and a motion parameter optimization instruction are dynamically synthesized, so that the dynamic parameter set is obtained. Wherein the trajectory correction instruction reconstructs a real-time interpolation path through a B-spline curve, the motion controller receives a space trajectory correction instruction and a motion parameter optimization instruction, at least three times of compensation iteration are completed in a single drilling period, a linear motor and a spindle motor are driven to execute online precision compensation, and the execution delay of the compensation instruction is smaller than 2 ms.

The invention discloses a machining error-oriented hardware part size compensation method and platform, and relates to the technical field of industrial control, in particular to the technical field of hardware part machining. The method comprises the steps that based on a to-be-machined part, structural basic characteristics are obtained, and a three-dimensional size compensation map is drawn up with the elasticity modulus of a material as a constraint; matching each tool path target compensation amount sequence based on the map, and synchronously predicting a size deviation value; introducing an environmental interference factor, and inputting the pre-training model to generate a dynamic correction coefficient; and on the basis of the data, cutter compensation, the feeding rate and the rotating speed are dynamically adjusted through a self-adaptive strategy till the machining error is stabilized within the tolerance range. The technical problem that in traditional hardware part machining, due to the fact that a static compensation strategy is difficult to adapt to a dynamic machining environment, the machining size precision is unstable is solved, and the technical effects that self-adaptive high-precision size compensation based on the real-time machining state and multi-source interference sensing is achieved, and the part machining consistency and the percent of pass are improved are achieved.

Technology is described for determining a task plan that is usable by a robotic device in a workspace. The method can include converting instructions received for the robotic device into temporal logic (TL) statements and to a non-deterministic Buchi Automaton. A task probabilistic machine learning model can be generated with feasible task plans using the non-deterministic Buchi Automaton. A plurality of task plans can also be created or generated using the task probabilistic machine learning model. A sensor probabilistic machine learning model of the workspace can be constructed using information from sensors of the robotic device. The task plans from the task probabilistic machine learning model can be compared with the sensor probabilistic machine learning model to select the task plan with a high probability of correlation to the workspace.

The present disclosure provides a humanoid robot comprising a torso having an alpha model deployed on a first GPU, and wherein said alpha model includes a first number of parameters and is configured to receive a natural language command from a human and generate processed data, a beta model deployed on a second GPU, and wherein said beta model includes a second number of parameters and is configured to receive the processed data from the alpha model and provide output data used to control an extent of the left wrist, and wherein the first number of parameters is larger than the second number of parameters, and a unified training framework is used to jointly train the alpha model and the beta model.

The invention relates to the field of metallurgical automation control, and discloses an intelligent metallurgical automation control method and system, and the method comprises the steps: collecting the real-time operation data of multiple working procedures of metallurgical production, carrying out the time alignment processing, and quantifying the correlation features between the working procedures, thereby obtaining an initial quantification feature set; dynamically simulating the feature set, acquiring intensity and direction parameters of process variable fluctuation conduction, and constructing a cross-process influence model; a global coordination instruction set is generated by combining downstream constraint feedback optimization distributed controller coordination parameters; updating a multi-process state vector, and if a local optimal risk exists, correcting a conduction path; and generating an optimal control scheme based on downstream real-time feedback iterative optimization, and distributing adjustment parameters to realize multi-process cooperative control. The process coupling relation can be accurately quantified, and the metallurgical production efficiency and the quality stability are improved.

The invention provides a five-axis series-parallel numerical control machine tool machining process virtual monitoring simulation method and system based on digital twinning, and the method comprises the steps: building an information interaction mechanism between a physical machine tool and a digital twinning model, enabling a server side to collect sensor data, and transmitting the real-time data to a client side through a communication protocol; the client processes the data and drives the digital twin model. And aiming at potential errors of twin system forecast, tool setting error compensation can be carried out in real time, so that the machining precision is improved. Meanwhile, the system supports an offline simulation function, combines an inverse kinematics algorithm and an NC code, fuses a material removal model, simulates a machining process, evaluates machining performance and errors, and provides an optimization basis for machining strategies of workpieces with different geometrical shapes and materials. According to the method, bidirectional interaction between the physical entity and the virtual model is achieved, the real-time monitoring and visualization capability of the machining process is enhanced, a reliable simulation evaluation and optimization means is provided for machining of complex parts, and the method has wide industrial application prospects.

The invention relates to the technical field of planet carrier boring machining, in particular to a planet carrier boring machining device and a boring machining method.The method comprises the steps that deviation characteristic values are determined on the basis of the difference between each machining parameter and a preset rated value and abnormal distribution of each machining parameter at all moments before all deviation to-be-corrected moments; on the basis of the correlation between each machining parameter at all moments before each deviation to-be-corrected moment and the cutting temperature and the vibration frequency, a characteristic coefficient is determined, and the deviation value of each machining parameter is determined by combining the difference between each machining parameter at each deviation to-be-corrected moment and a preset rated value and the deviation characteristic value; and each machining parameter at each deviation to-be-corrected moment in the boring machining process is regulated and controlled. The machining deviation is actively compensated by fusing the cutting temperature and the vibration frequency in real time, and the machining precision of planet carrier boring is improved.

The invention provides a cylinder processing cutter path adaptive optimization generation method, which belongs to the technical field of cylinder processing, and adopts fractal dimension analysis to identify surface geometric complexity and determine a minimum repetitive unit, and applies a four-color theorem to carry out non-intersection area division on the surface, so as to improve the processing precision of the cylinder processing cutter path. Multi-axis linkage machining parameters are initialized, global path planning and cutter axis vector optimization and collision detection are performed based on an improved Di jkstra algorithm, path adaptive adjustment is performed according to local curvature change of a minimum fractal matrix, a game model is established, and coevolution of a double-layer game model is realized by adopting a cross optimization rule. And finally, an optimized tool path NC code containing the position coordinates, the attitude angle and the feeding speed is generated, and the technical problem that the machining efficiency and the surface quality are difficult to consider in tool path planning under the complex geometrical characteristics of the cylinder surface is solved.

The invention discloses a ship main power and auxiliary power real-time switching method and system based on large model optimization, and relates to the technical field of ship power system control, and the method comprises the steps: collecting the operation state data and environment parameters of a ship main power system and an auxiliary power system in real time through a multi-mode sensor array; and performing space-time alignment and noise suppression processing on the operation state data and the environment parameters, inputting the feature subset into a lightweight large model inference engine, generating a multi-target optimization decision set, executing a main and auxiliary power dynamic switching control sequence in a preset time window according to the multi-target optimization decision set, and outputting the main and auxiliary power dynamic switching control sequence. And generating a self-optimization log including a switching efficiency evaluation report and a model iteration suggestion based on the deviation degree of the switching feedback data and a preset performance index. According to the ship main and auxiliary power real-time switching method and system based on large model optimization, stable operation of a ship power system is guaranteed, and meanwhile the service life of equipment is prolonged.

The invention relates to the technical field of offshore wind turbine inspection, and discloses an offshore wind turbine inspection monitoring system based on digital twinning. A twinborn data synchronization module of the system collects fan structure state data and environmental hydrological data in real time through a multi-source sensor cluster, and constructs an offshore fan digital twinborn body; the routing inspection path dynamic generation module analyzes a fan fragile area and risk distribution based on the digital twinborn body, and generates a multi-unmanned aerial vehicle cooperative routing inspection path in combination with real-time marine weather forecast; the unmanned aerial vehicle cooperative control module schedules an unmanned aerial vehicle group to execute a task according to the cooperative inspection path, and dynamically responds to real-time data updated by the twin data synchronization module; and the real-time abnormity identification module fuses and analyzes unmanned aerial vehicle returned data, identifies fan defects, outputs abnormal information, calculates relative space deviation, generates an attitude control instruction, and sends the attitude control instruction to the unmanned aerial vehicle cooperative control module to realize unmanned aerial vehicle attitude real-time adjustment.

A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.

The invention relates to an unmanned aerial vehicle self-adaptive wind-resistant flight control method and system based on multi-mode environment perception. The method comprises the steps that a miniature wind speed and direction sensor array obtains real-time wind speed and wind direction information of the surrounding environment of an unmanned aerial vehicle; the inertial measurement unit obtains real-time attitude angle, angular velocity and acceleration information of the unmanned aerial vehicle; the visual flow sensor obtains real-time image data of the surrounding environment of the unmanned aerial vehicle; the data fusion and wind field estimation module receives multi-source data collected by the miniature wind speed and direction sensor array, the inertial measurement unit and the visual flow sensor, carries out fusion processing on the multi-source data based on an extended Kalman filtering algorithm, and predicts to obtain predicted wind field information in a future short time window; the flight control core module adopts an adaptive sliding mode control algorithm to calculate a compensation control quantity for counteracting wind disturbance, and performs real-time control on the unmanned aerial vehicle based on the compensation control quantity; and the wind resistance and the operation reliability of the unmanned aerial vehicle in a complex dynamic environment are improved.

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

The invention discloses a battery cell simulation type state sensing and feedback method and system, and the method comprises the steps: providing a simulation battery cell detection device which flows on a production line along with an actual battery cell; the device is provided with a shell matched with an actual battery cell in boundary dimension, a film pressure sensor covering the surface of the shell, and a built-in collector, in the circulation process, the sensor collects pressure data applied by the production line equipment in real time; the built-in collector stores data and sends the data to the upper computer processing system in a wireless mode; and the upper computer processing system receives and analyzes the pressure data, judges abnormity according to a preset pressure threshold value and generates a report. By simulating the real stress condition of the battery cell, comprehensive, real-time and automatic monitoring of the pressure state in the production process is realized, the abnormal reason can be accurately positioned, the problems of lack of monitoring means and difficulty in problem tracing in the prior art are solved, the product quality and the production efficiency are effectively improved, and the potential quality hazard is reduced.

The invention belongs to the technical field of laser processing, and particularly relates to a laser processing control method based on a neural network, and the method specifically comprises the following steps: a target definition and input stage; a physical model and database stage: establishing a basic model library; establishing a mapping database; in the AI core engine stage, a training model learns a complex nonlinear relation among laser parameters, material response and a final processing result; searching an optimal laser parameter combination by using an optimization algorithm based on the prediction model and a target set by a user; a laser parameter automatic adjustment and execution stage; a real-time monitoring and feedback stage; and an iterative learning and system improvement stage. The invention further discloses a control system, electronic equipment and a computer storage medium. According to the method, submicron precision control is achieved, thermal damage can approach to zero, the development period is shortened, online real-time regulation and control are achieved, energy consumption is reduced, the material utilization rate is increased, and the method has an interpretable decision-making mechanism and cross-material generalization ability.

Methods of performing a dispatched logistics operation for delivery / pickup of items using a modular autonomous bot apparatus assembly (with a modular autonomy module, cargo storage system, auxiliary power module, and mobility base) and a dispatch server. The modular autonomy control module receives a dispatch command from the dispatch server and authenticates that each of the modular components in the assembly are compatible with the particular dispatched logistics operation. The bot assembly receives the transported item, autonomously moves from an origin location to a destination per the dispatch command using the mobility base autonomously controlled by the modular autonomy control module, receives authentication input that at least correlates to authentication information in the dispatch comment to allow selective access to the modular cargo storage system of the bot assembly, and then autonomously returns to the origin after the item is detected removed from the cargo storage system.

The invention discloses a commercial building energy monitoring and intelligent control method and device and a storage medium, and belongs to the technical field of building intelligent control, and the method comprises the steps: collecting data, building a nonlinear mapping relation, and generating an energy consumption demand prediction tensor; injecting an adversarial disturbance sample, and evaluating the robustness of the prediction model; in combination with the energy consumption baseline, performing cross confirmation and correction on the prediction data exceeding the threshold value; performing attribution analysis on the corrected energy consumption sequence to generate an energy consumption attribution map; adjusting the solution of a multi-objective optimization function according to the atlas, and generating an optimal cooperative control strategy; and the comprehensive efficiency is used as a reinforcement learning reward, strategy parameters are iteratively updated, and a control knowledge base is formed. According to the method, a closed-loop control framework integrating robust demand prediction, dynamic attribution analysis, collaborative optimization decision and a self-evolution strategy is adopted, intelligent regulation and control of building energy consumption can be realized, and the long-term adaptive optimization capability is improved.

The invention relates to the technical field of industrial automation and intelligent control, in particular to a general control system strategy optimization method based on reinforcement learning, and the method comprises the steps: firstly collecting the operation data of a system, constructing a state vector of reinforcement learning, and enabling a reinforcement learning agent to fully understand the current operation condition of the system; then, the state vector is input into a reinforcement learning strategy network, an action for adjusting a control strategy is generated by the network, and the action can be used for modifying the proportion, integral or differential coefficient of PID and can also be used for adjusting the prediction step length, weight coefficient or constraint strength of model prediction control, so that the adaptive capacity of a controller to external changes is enhanced; then, a reward signal is constructed according to a response result of the reference controller; the reward function comprehensively considers the error size, the steady-state characteristic, the system energy consumption, the control smoothness and the stability requirement, so that the reinforcement learning not only pays attention to the error minimization when optimizing the strategy, but also considers the low energy consumption, the smooth action and the anti-interference performance at the same time.