180 results about "Adaptive control algorithm" patented technology

The invention discloses an automatic parameter adjustment test method for an electromagnetic angular displacement sensor, and the method comprises the steps: injecting a standard angle signal into a to-be-tested sensor, and collecting an output response signal; calculating a deviation value of the sensor parameter based on the output response signal and the standard angle signal, and judging whether the deviation value exceeds a preset threshold value or not; if yes, based on the deviation value, a control instruction used for compensation is generated through a self-adaptive control algorithm, and the mechanical zero position of the to-be-detected sensor or the relative position of an internal electromechanical conversion element is adjusted; based on the adjusted output response signal, the above steps are repeatedly executed, and sensor parameters are iteratively updated; and when the deviation value is lower than a preset threshold value, judging that the output precision meets a preset requirement, storing the current parameter, and completing the parameter adjustment process of the to-be-detected sensor. By constructing a closed-loop control flow of'detection-analysis-adjustment-verification 'and an adaptive control algorithm, full-automatic, high-precision and high-efficiency calibration of the zero position, sensitivity and excitation signal parameters of the electromagnetic angular displacement sensor is realized.

The invention provides a tunnel support self-adaptive adjusting method and system based on rock mass-support feedback, and relates to the technical field of tunnel engineering support. The method comprises the following steps: acquiring surrounding rock strain data and stress data through a sensor network arranged in tunnel surrounding rock, and generating original monitoring data; calculating the surrounding rock strain energy density by using a data processing unit; matching the strain energy density with a preset energy stage feature through an adaptive control algorithm to generate an energy stage matching result; determining an energy state stage based on a stage confirmation module and generating an energy stage identifier; a supporting mode switching instruction is generated through a supporting mode mapping querier; and finally, the supporting control unit adjusts the working parameters of the supporting structure, real-time monitoring of the energy state of the surrounding rock and dynamic matching of the supporting parameters are achieved, the adaptability and safety of tunnel supporting are effectively improved, and the hysteresis of manual intervention is reduced.

The invention discloses a self-adaptive sensing anti-shake regulation and control method and system for a spinal surgery auxiliary mechanical arm, and relates to the technical field of spinal surgery, and the method comprises the steps: collecting multi-dimensional data of a mechanical arm tail end actuator through a multi-dimensional sensor group, and transmitting the multi-dimensional data to an AeroStead space-level stability calculation platform; the platform carries out filtering processing on the data, and vibration of the mechanical arm and disturbance components caused by external interference are separated; constructing a spine mechanical arm rigid-flexible coupling dynamic disturbance observation and compensation model by using the disturbance component to observe disturbance and generate a compensation amount; calling a spinal operation area three-dimensional reconstruction and dynamic obstacle avoidance mixed density network model, and fusing different images to generate an operation area three-dimensional dynamic model and an obstacle avoidance adjustment signal; a mechanical arm motion control instruction is generated through a force sense position cooperation self-adaptive control algorithm; and the mechanical arm is driven to move for closed-loop regulation and control. According to the method, through fusion of multiple models and algorithms, the disturbance compensation precision, the operation area modeling accuracy and the control collaboration are improved, and the operation safety is guaranteed.

The invention relates to the technical field of power system control, in particular to a direct-current capacitor self-synchronizing grid-connected control strategy method, system, equipment and medium, a direct-current capacitor energy dynamic model is established based on an actual energy balance relation, a direct-current voltage signal is acquired in real time, and a synchronous compensation signal is calculated based on the acquired direct-current voltage signal; performing phase compensation and gain adjustment on the synchronous compensation signal through a correction link based on the calculated synchronous compensation signal, adjusting the internal frequency of the inverter based on the synchronous compensation signal after phase compensation and gain adjustment, controlling the DC voltage through the internal frequency of the inverter, and establishing a self-synchronization control model; and based on the self-synchronization control model, establishing an adaptive control algorithm, generating a damping coefficient and a virtual inertia coefficient through the adaptive control algorithm, and dynamically adjusting control parameters according to the damping coefficient and the virtual inertia coefficient.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention relates to the field of oil field heating furnace control, and discloses an oil field heating furnace intelligent digital implementation method and control method.The control method comprises the following steps that S1, a temperature sensor is used for monitoring the temperature of an oil field heating furnace in real time; s2, calculating a temperature error between the upper limit and the lower limit of the real-time temperature and the set temperature according to the monitoring data of the temperature sensor; s3, a self-adaptive PID control algorithm is applied, and heating power is calculated based on the temperature error; and S4, dynamically adjusting the heating power to control the temperature of the oil field heating furnace. The precision and stability of temperature control can be improved, the oil field heating furnace can control the target temperature more accurately, and the production efficiency is improved. Meanwhile, the self-adaptive PID control algorithm can automatically adjust parameters according to actual working conditions and environment changes, the adaptability and robustness of the system are improved, and the requirements for manual intervention and adjustment are reduced. In addition, the stable and efficient heating process can be achieved by dynamically adjusting the heating power, and energy and cost are saved.

The embodiment of the invention provides a flywheel energy storage system and an electrical control method and system thereof, and the electrical control method comprises the steps: collecting the operation state data of the flywheel energy storage system in real time, and carrying out the preprocessing of the operation state data; inputting the preprocessed operation state data into a parameter adaptive control algorithm, dynamically adjusting control parameters of the algorithm through fuzzy logic, and generating a control instruction for controlling the power converter; based on the control instruction and the real-time demand of a power grid, cooperatively controlling the output power and the direct-current voltage of the flywheel energy storage system, and when it is detected that the direct-current voltage deviates from a target range, automatically switching an electrical control mode to a voltage stabilization mode for stabilizing the direct-current voltage within the target range; and monitoring the operation state of the flywheel energy storage system in real time, and triggering a protection mechanism when at least one abnormity of overvoltage abnormity, overcurrent abnormity or mechanical fault abnormity is detected.

The invention discloses a linkage system of a roadway air distribution device and a dust removal device and a control method, and belongs to the technical field of safety and environmental protection of mine / tunnel construction. The system comprises a long-pressure air control system, a short air draft dust collection system and an intelligent control module, the long-pressure air control system forms an airflow air wall through a long-distance air distribution device to restrain dust diffusion, the short-air-draft dust collection system captures dust through a short-distance dust collection cover, and the intelligent control module dynamically adjusts operation parameters of the long-pressure air control system and the short-air-draft dust collection system based on parameters such as dust concentration and air speed. The control method comprises the stages of dust suppression, capture, weak wind area removal and intelligent adjustment, and efficient dust removal is achieved through airflow collaborative optimization and a self-adaptive control algorithm. The dust collecting rate can be increased by 50% or above, energy is comprehensively saved by 20% or above, potential safety hazards in weak wind areas are eliminated, and the device is suitable for dust-producing scenes such as driving working faces and belt conveying roadways and has the advantages of being efficient, safe, capable of saving energy and wide in applicability.

The invention relates to the technical field of drilling, and provides a roofbolter feedforward-feedback model-free adaptive tracking control method and system based on rotation force. According to the method, a valve-controlled hydraulic propulsion control model for driving a hydraulic cylinder and a corresponding reversing valve in the working process of the hydraulic jumbolter is established, and a nonlinear autoregressive moving average model between the propulsion force in the working process of the hydraulic jumbolter and the output driving current of the reversing valve is obtained through the valve-controlled hydraulic propulsion control model; through a nonlinear autoregression moving average model of the hydraulic jumbolter, an output identification observer deployed with a data-driven adaptive control algorithm of the hydraulic jumbolter is established, and the output identification observer performs adaptive tracking control on the working state of the hydraulic jumbolter through the deployed data-driven adaptive control algorithm. The tracking control problem of the valve control electro-hydraulic servo unit under the complex working condition is effectively solved.

An intelligent heat pump system autonomously manages thermal regulation in heating and cooling modes using real-time sensor data and adaptive control algorithms. The system comprises a heat pump integrated with components including a supply valve, regulating valve, return valve, and compressor expander, along with multiple sensors configured to monitor temperature and pressure conditions at various points, including the reservoir, cold side, and hot side. A removable memory stores a directive file containing operational parameters, including target temperatures, valve timing, and pressure control data. A control unit with a processor executes firmware modules that include sensor calibration routines, motor and valve control logic, and an adaptive algorithm that continuously compares real-time sensor input against target parameters. Based on deviations, the system dynamically adjusts component operations to regulate refrigerant flow, internal energy, temperature, and pressure, thereby driving the system toward a steady-state thermal condition. This autonomous system ensures efficient performance, enhanced temperature stability, and optimal energy usage across a range of varying environmental conditions.

The invention discloses a biogas energy comprehensive utilization efficiency optimization system and method based on the Internet of Things, and belongs to the technical field of industrial automation control and energy management, the system realizes intelligent management and optimal scheduling of the whole life cycle of biogas energy through a distributed multi-level calculation control architecture, and the core is a multi-agent cooperative control platform. Comprising a fermentation agent, a purification agent, a storage agent and a utilization agent, all the agents can independently execute local control tasks and can cooperatively work to achieve global optimization, an Internet of Things sensor is deployed in the system to collect operation state data, real-time data transmission is achieved through the industrial Ethernet, and the intelligent control system is established. The marginal intelligent control node carries out local preprocessing and real-time decision making, a self-adaptive control algorithm library is innovatively integrated, the optimal control algorithm is dynamically selected according to different process links, the biogas yield and quality are remarkably improved, and practical application shows that the biogas yield is improved by 15%-25%, and energy consumption is reduced by 10%-18%.

The invention provides a self-adaptive control method and system for equipment operation parameters, and relates to the technical field of self-adaptive control of oil field operation parameters, and the method comprises the steps: obtaining multi-source data such as structure displacement, process state parameters, component analysis data and fluid performance parameters to evaluate a current process stable state, and outputting a state quantized value; then, in combination with the component analysis data, the state quantized value and the current value of the fluid performance parameter, a pre-trained decision model is used for analysis and decision making, and an expected target value which the fluid performance parameter should reach is generated; and finally, the expected value serves as a leading target, feedback of process state parameters is sensed in real time, operation parameters of key process execution mechanisms such as a drilling machine and a slurry pump are dynamically adjusted through a self-adaptive control algorithm, conversion from passive response to active optimization in the drilling process can be achieved, and the well wall stability and the drilling efficiency are effectively improved.

The invention discloses an RBF neural network active suspension control method and system for complex road conditions. The method comprises the steps that vehicle body motion information is obtained in real time; the RBF neural network self-adaptive controller adjusts internal control parameters of the RBF neural network self-adaptive controller in an online self-adaptive mode based on the deviation between the vehicle body motion information and the expected stable state, and outputs an adjusting instruction used for adjusting a supporting mechanism; and according to an adjusting instruction output by the RBF neural network self-adaptive controller, the supporting mechanism is driven to do telescopic motion so as to adjust the vertical, pitching and side-tipping postures of the carrying trolley. By introducing the RBF neural network adaptive control algorithm, real-time sensing and intelligent processing of the vehicle body motion information are realized, and the control parameters of the suspension system can be dynamically adjusted according to the complex road condition, so that the stability and safety of the carrying trolley under the complex road condition are effectively improved, the risk of damage to instruments and equipment is remarkably reduced, and the service life of the carrying trolley is prolonged. The transportation efficiency is improved.

The invention discloses a building environment real-time regulation and control method, device and equipment based on the Internet of Things and a medium, and relates to the technical field of intelligent building control. Light, temperature and humidity data of a building environment are collected in real time through Internet of Things sensing equipment, frequency domain features of an environment data set are extracted through Fourier transform, classification and recognition are carried out in combination with a preset environment regulation and control model, and a dynamic weight algorithm is adopted to optimize and generate an environment control signal. And through a self-adaptive PID control algorithm of Kalman filtering denoising and reinforcement learning optimization, a deviation integral is calculated in real time to generate a precise regulation and control instruction, and environment regulation and control equipment is synchronously driven. Frequency domain feature analysis, dynamic weight adjustment and adaptive control technologies are fused, multi-parameter coordinated regulation and control of the building environment are realized, and the real-time performance, the stability and the energy efficiency ratio of environment regulation and control are effectively improved.

The invention discloses an intelligent control method of a numerical control dotting and ruling machine for mold machining, and particularly relates to the field of numerical control machining control. The method comprises the following steps: establishing a three-dimensional point cloud model containing surface microscopic curvature distribution and a local stress-strain relationship by acquiring a space geometric model and material physical parameters of a target mold workpiece; performing grid division on the point cloud model, extracting curvature features and local elastic modulus of each grid unit, and constructing a parameter matrix; in combination with the initial track of the preset tool path, modulating by adopting a self-adaptive control algorithm to generate a corrected path; in the machining process, a control vector is constructed based on the corrected path and the dotting depth and the scribing force which are fed back in real time, an execution mechanism is driven to conduct closed-loop control, and dynamic compensation of each dotting and scribing action is achieved; the method can effectively solve the problems of uneven dotting depth, lineation deformation and quick tool abrasion in the machining process of complex curved surfaces or high-strength materials, and the machining precision and stability are remarkably improved.

The invention provides an optimal control system and method for brewing fermentation of gingko and tartary buckwheat vinegar, and relates to the technical field of food fermentation engineering. The method comprises the following steps: S1, on the basis of a mixed raw material of a fed ginkgo biloba extract and tartary buckwheat powder, acquiring raw material component data in real time by adopting an online spectrum analysis technology, associating initial environment parameters by a multivariable fusion analysis method, and generating raw material characteristic fingerprints and an optimal starting environment parameter set; raw material component data are obtained in real time through an online spectral analysis technology, initial environment parameters are associated with a multivariate fusion analysis method, accurate generation of raw material characteristic fingerprints and optimal starting environment parameters is achieved, adaptability and stability of the fermentation starting stage are remarkably improved, real-time calculation of a dynamic time sequence prediction model on the pH change trend is achieved, and the stability of the fermentation starting stage is improved. And a parameter adaptive control algorithm is matched.

The invention provides a two-dimensional formation composite sliding mode control method under an explicit convergence time limit, and relates to the technical field of automatic driving cooperative control. The method comprises the following steps: firstly, acquiring real-time motion state information of a pilot vehicle and N following vehicles, and establishing a third-order nonlinear dynamic model of each following vehicle; calculating a tracking error of the following vehicle based on the virtual vehicle strategy; dynamically processing the predetermined performance constraints; constructing a composite finite time sliding mode surface of a layered structure, and determining a total execution time limit required by convergence of all monitoring signals in the two-dimensional vehicle formation control system from an initial deviation state to a predefined steady state region; and finally, on the basis of the composite finite time sliding mode surface, an external disturbance boundary is estimated online in combination with an adaptive control algorithm, a control instruction is generated, and a monitoring signal is forced to reach a steady state in an explicit time upper boundary. The method provides a visual performance estimation means for practical engineering application, and improves the stability, rapidity and accuracy of two-dimensional formation control.

The invention discloses a spin-coating acceleration self-adaptive control method based on liquid level disturbance feedback. The method comprises the following steps: collecting a liquid level disturbance signal; extracting liquid level parameters and obtaining a disturbance energy spectrum; establishing a disturbance-film thickness correlation function; designing an adaptive control algorithm; and performing closed-loop execution and optimization. According to the method, the liquid level morphology change data is obtained by using a high-speed camera and a laser reflection method, and the disturbance amplitude, frequency and phase characteristics can be extracted in real time, so that the liquid level stability is quantitatively represented. According to the method, a correlation model between liquid level disturbance parameters and film thickness evolution is established. By coupling the disturbance amplitude and hydrodynamic characteristic parameters (such as Reynolds number, Marangoni number, viscosity and volatilization rate), the corresponding relation between the disturbance intensity and the film thickness deviation is constructed, and the film forming trend of different spin coating stages can be dynamically predicted. According to the invention, a self-adaptive acceleration control algorithm based on disturbance feedback is adopted, and closed-loop optimization control of the spin-coating process is realized.

The invention relates to an unmanned aerial vehicle load optimization control system based on multi-sensor fusion, and the system employs an electro-optical sensor, an infrared sensor and a laser radar to collect multi-source data, and generates a state vector through the fusion of an extended Kalman filtering algorithm. A multi-source energy consumption model including a power system, a cradle head execution mechanism and load operation is established, the total energy consumption is calculated in real time, and a model prediction control framework is adopted to optimize a release strategy including the position, the opportunity and the attitude. An L1 self-adaptive control algorithm is introduced, modeling errors and external disturbance are compensated in real time, the response time is smaller than 50 ms, and the task completion rate reaches 87%. The system adopts a modular design and is compatible with an ROS 2 architecture and a PX 4 architecture, the positioning error is less than 1m, the delivery error radius is less than 5m, and the energy consumption is reduced by 15-25%.

The present application belongs to the technical field of transformer manufacturing, and provides a transformer drying time sequence prediction method and system, which comprises the following steps: obtaining transformer drying full-process information from multiple sources to form an original data set, obtaining and classifying the original data set according to corresponding classification information and performing good product screening to obtain a good product data group and perform data processing and integration to obtain a learning data set containing an input feature group, a drying time sequence vector group and an energy consumption data group, constructing a drying prediction model based on a hybrid neural network, training the drying prediction model using the learning data set, and judging whether the prediction accuracy of the drying prediction model meets the standard, if the standard is met, performing reasoning prediction to obtain a predicted drying time sequence vector and predicted energy consumption data, judging whether the drying process parameters need to be optimized, if the drying process parameters need to be optimized, performing collaborative optimization and adjustment of the drying process parameters based on the prediction results by using a self-adaptive control algorithm in combination with the drying prediction model.

The invention discloses an exercise assisting system for limb rehabilitation, and relates to the technical field of intelligent rehabilitation medical instruments, and the exercise assisting system comprises a multi-dimensional sensing module, a central processing module, an auxiliary execution module, a man-machine interaction module and a data tracing module; the multi-dimensional sensing module is used for collecting motion state data and physiological safety data of a patient in real time; and the central processing module is electrically connected with the multi-dimensional sensing module and is used for sensing the motion state data and the physiological safety data according to the motion state data and the physiological safety data. According to the exercise assisting system, comprehensive and synchronous collection of the exercise state and physiological safety data of the patient is achieved by integrating the multi-dimensional sensing module, solid data support is provided for precise rehabilitation, the adopted self-adaptive control algorithm can be closely combined with the current rehabilitation stage of the patient, auxiliary parameters can be dynamically adjusted, and the rehabilitation accuracy of the patient is improved. High personalization and accurate adaptation of the rehabilitation strategy are ensured, and the long-term effect of the rehabilitation scheme is effectively improved.

An intelligent heat pump system autonomously manages thermal regulation in heating and cooling modes using real-time sensor data and adaptive control algorithms. The system comprises a heat pump integrated with components including a supply valve, regulating valve, return valve, and compressor expander, along with multiple sensors configured to monitor temperature and pressure conditions at various points, including the reservoir, cold side, and hot side. A removable memory stores a directive file containing operational parameters, including target temperatures, valve timing, and pressure control data. A control unit with a processor executes firmware modules that include sensor calibration routines, motor and valve control logic, and an adaptive algorithm that continuously compares real-time sensor input against target parameters. Based on deviations, the system dynamically adjusts component operations to regulate refrigerant flow, internal energy, temperature, and pressure, thereby driving the system toward a steady-state thermal condition.

The invention provides a double-drive synchronous control method and system for a gantry machine tool, and the method comprises the steps: S1, building a flexible multi-body dynamic model of the gantry machine tool through combining the part flexibility and connection flexibility of the gantry machine tool; s2, constructing a dual-drive electromechanical coupling model of the gantry machine tool by combining servo system characteristics of the gantry machine tool and traditional system dynamic characteristics on the basis of the flexible multi-body dynamic model; and S3, according to the double-drive electromechanical coupling model, a self-adaptive control algorithm combining cross coupling double-drive synchronous control and sliding mode control is adopted to complete synchronous control of a double-drive system. According to the gantry machine tool double-drive synchronous self-adaptive control algorithm based on cross coupling double-drive synchronous control and sliding mode control, a cross feedback item is introduced, so that information of two drivers is correlated, the problem of double-drive synchronous error accumulation faced by a traditional double-drive parallel control or master-slave control method of a gantry machine tool is solved, and the system reliability is improved. The synchronous control of the gantry machine tool servo double-drive system is realized.

This invention discloses an adaptive adjustable hip joint rehabilitation training device and its control method, belonging to the technical field of rehabilitation training equipment. The device mainly includes a seat board, mounting plate, casters, universal wheels, backrest, electrically adjustable telescopic pillow, tension straps, electrically adjustable armrests, a power supply drive terminal box, a drive control terminal box, and symmetrically arranged multi-dimensional adjustment mechanisms. The drive control terminal box has built-in sensing components and an adaptive control algorithm, which can collect dynamic parameters such as the user's hip joint flexion and extension angles, movement speed, and joint force in real time, and automatically adjust the operating status of each drive component. This invention can achieve multi-angle and multi-directional hip joint posture adjustment, dynamically match the rehabilitation training intensity according to the user's tolerance, and automatically trigger safety protection when parameters are abnormal to avoid the risk of secondary injury. The device is flexible and adaptable to patients of different body types and rehabilitation stages. It is easy to operate, comfortable, and highly practical, effectively improving the safety and rehabilitation effect of hip joint rehabilitation training.

The invention discloses a self-adaptive control method and system for an outdoor energy storage converter, and the method comprises the steps: a current inner loop control process: carrying out the decoupling control of a current inner loop of the outdoor energy storage converter through a feedforward decoupling control strategy based on a dq model of the outdoor energy storage converter; in the voltage outer loop control process, adjustable parameters of the outdoor energy storage converter are obtained, the adaptive law of the outdoor energy storage converter is obtained through a Narendra stable adaptive control algorithm, and the influence of parameter changes of a controlled object on a control result is counteracted by continuously changing the adjustable parameters; in the smooth switching control process, the output voltage of the outdoor energy storage converter is adjusted by obtaining the voltage frequency, amplitude and phase information of the power grid side and / or the load side according to the current inner loop control process and the voltage outer loop control process, and grid-connected and off-grid switching is conducted on the outdoor energy storage converter; according to the invention, the stable operation of the outdoor energy storage converter is ensured, and the system robustness is enhanced.

The invention discloses a self-adaptive control method and system for a composite material fiber placement machine based on digital twinning, and belongs to the technical field of automatic fiber placement forming of composite materials. The system comprises a sensing layer, an edge computing layer and a cloud digital twin platform, the sensing layer is used for collecting multi-source data in the laying process in real time; the edge computing layer is used for processing the multi-source data in real time to obtain processed data and storing local data; and the cloud digital twin platform is used for performing dynamic simulation based on the processed data and predicting the rebound deformation and defect probability of the prepreg. The system adjusts process parameters in real time through a self-adaptive control algorithm to form closed-loop control. The problems of laying track deviation and frequent defects caused by fixed parameter control of a traditional fiber laying machine are solved, accurate regulation and control of the laying process are achieved, and the laying quality and the production efficiency are remarkably improved.

The application discloses a gun positioner wireless charging method and system and a charging seat, relates to the technical field of public safety intelligent equipment, and realizes real-time and synchronous acquisition of charging seat sensor array data and charging process parameter data; according to an in-place monitoring signal, an RFID induction circuit reads data of an RFID label built in a positioner, data binding is completed in combination with a preset gun number database, and a real-time gun number-charging state binding result is generated; the binding result and the charging process parameter data are constructed into structured charging state data flow and transmitted to a remote server to generate a remote instruction; and based on the remote instruction, a charging control instruction is dynamically generated through an adaptive control algorithm. The application realizes unique binding of a gun number and a charging state through RFID label binding and a double sensor, eliminates data mismatch caused by position change, compares pressure and infrared signals to verify that the positioner is in place, solves the problems of missing identity recognition and rigid in-place detection, and improves the accuracy of gun management.

The application discloses a remote unmanned plane control method and device, and the method comprises the following steps: acquiring a local control scene, wherein the local control scene is obtained by processing three-dimensional environment information of the remote unmanned plane by using a compressed sensing control paradigm; acquiring an electroencephalogram signal generated when an operator watches the local control scene; analyzing the electroencephalogram signal by using an intention analysis algorithm to obtain original control instructions of the remote unmanned plane by the operator; optimizing the environment information and the original control instructions by using an environment self-adaptive control algorithm to obtain continuous unmanned plane control instructions; and sending the continuous unmanned plane control instructions to the remote unmanned plane and controlling the remote unmanned plane based on a kinematics model. The application effectively solves the problem of poor space perception and control ability of the existing brain-controlled remote unmanned plane technology.

The application discloses a numerical control machine tool control system based on big data, a data acquisition module acquires tool wear during production of the numerical control machine tool, load information of the machine tool during machining, machining precision error of workpiece machining and a machining precision error change rate; relates to the technical field of intelligent control, the system comprehensively utilizes big data and a fuzzy self-adaptive control algorithm, the fuzzy self-adaptive control algorithm determines a feed rate adjustment range based on fuzzy information of a machining state, selects a feed rate scheme with the highest fitness function in a scheme set containing all possible feed rates by setting the fitness function, and a feed rate correction adjustment module dynamically adjusts the feed rate according to actual machining precision, so that the difference between the feed rate scheme determined by the fitness function and actual machining is made up. Through the synergistic effect of these technologies, the machining precision and efficiency of the numerical control machine tool are effectively improved.

The invention discloses a copper-clad plate lamination defect real-time identification and process closed-loop regulation and control method and system, and the method comprises the steps: synchronously collecting a visible light image sequence and infrared temperature field distribution data in a lamination process through a multi-mode sensing system, and generating a time-space aligned vision-thermal image fusion data stream; on the basis of the vision-thermal image fusion data stream, identifying the type, position and severity of the lamination defect of the copper-clad plate on line by adopting a lightweight convolutional neural network, and generating a detection result containing a defect feature vector; inputting a detection result into a lamination process digital twinborn model, and generating defect evolution influence prediction data; and dynamically adjusting the pressure distribution of the laminating machine and the temperature curve of the heating unit through an adaptive control algorithm based on the defect evolution influence prediction data. By utilizing the embodiment of the invention, the crossing from passive defect detection to active prevention and accurate regulation and control can be realized, and the production quality, efficiency and product consistency of the copper-clad plate are improved.