78 results about "Adaptive feedback control" patented technology

The invention provides a Python monitoring task resource use method and system, and relates to the technical field of intelligent monitoring and resource optimization, and the method comprises the steps: extracting process call stack information through a multilayer convolutional neural network, constructing a process dependency graph, calculating a process criticality score, and recognizing a core monitoring process. And obtaining resource use data of the core monitoring process through a resource monitoring coroutine, determining an optimal sampling period by adopting a self-adaptive feedback control algorithm, and writing the data into a time sequence database. And performing data aggregation analysis by using a sliding time window algorithm to obtain a performance index, and constructing an anomaly detection model. Dimensionality reduction is carried out based on a principal component analysis method to construct feature vectors, and load types are divided by adopting a clustering algorithm; and generating a differentiated process optimization strategy according to the feature vector and the load type. When performance fluctuation is detected, an optimization strategy is executed, the anomaly detection model and the deep reinforcement learning model are updated according to the performance improvement index, and finally an optimization diagnosis report is generated.

The invention relates to the technical field of intelligent edge computing, and discloses a heterogeneous data processing optimization system based on intelligent edge computing. Comprising a heterogeneous data acquisition module, an edge preprocessing module, an intelligent optimization scheduling module, a heterogeneous fusion analysis module and a self-adaptive feedback control module. According to the system, data streams of different formats and protocols can be collected from a plurality of heterogeneous data sources in real time, and multi-modal correlation analysis is carried out after preprocessing and intelligent scheduling. Meanwhile, the system performance is optimized through self-adaptive feedback control, and a resource efficiency evaluation module and a safety compliance verification module are further arranged. According to the method, the problems of delay, accuracy, resource allocation, safety compliance and the like in heterogeneous data processing are effectively solved, the data processing efficiency and the system stability are improved, and the method is suitable for heterogeneous data processing scenes in the fields of industry, traffic, medical treatment and the like.

The invention discloses a building engineering human resource intelligent management optimization method based on big data analysis, and the method comprises the following steps: S1, collecting building engineering data, and carrying out the preprocessing of the data; s2, fusing multi-modal data, and synchronously generating time-space correlation optimization data; s3, constructing a construction site multi-scale digital twinborn model; s4, constructing a scheduling decision system, and simulating work type, task and resource interaction; s5, optimizing a personnel scheduling configuration strategy in combination with time constraint and skill matching; s6, performing Bayesian reasoning risk assessment, and generating a risk causal analysis report; and S7, the information is fed back to a manager through the multi-terminal display platform. Through big data analysis, digital twinborn modeling, reinforcement learning optimization and adaptive feedback control, intelligent scheduling and dynamic optimization of constructional engineering human resources are realized, construction efficiency is improved, resource waste is reduced, and construction safety and management intelligence level are enhanced.

The invention discloses a construction site digital-to-analog conversion and collaborative management method based on a distributed computing architecture. The method comprises the following steps: S1, collecting microscopic level data of a construction site; s2, obtaining mid-level data of the construction site; s3, acquiring macroscopic hierarchical data, and constructing a multi-scale spatio-temporal data set; s4, decomposing and processing the multi-scale spatio-temporal data set by using a spatio-temporal diagram convolutional network, and constructing a dynamic digital twin model; s5, designing a competition and cooperation mechanism between agents by using a game theory model, solving resource allocation conflicts through Nash equilibrium, and dynamically optimizing resource scheduling; s6, optimizing a scheduling strategy of the intelligent agent through a distributed algorithm based on near-end strategy optimization; s7, detecting errors in digital-to-analog conversion and scheduling processes by using a belief propagation algorithm; and S8, optimizing a scheduling strategy through a distributed adaptive feedback control mechanism. According to the invention, a distributed computing architecture, space-time diagram convolution, an intelligent scheduling technology and the like are adopted, and digital-to-analog conversion and resource management of the construction site are optimized.

The invention provides a personalized anesthesia management method and system. The method comprises the following steps: collecting real-time physiological data flow and previous medical record information of a patient to preliminarily evaluate operation requirements, and generating a preliminary anesthesia scheme; performing simulation prediction processing on the individualized reaction mode through a state-action value function updating mechanism by applying a deep Q network algorithm, and optimizing anesthetic selection and dosage by adopting a dynamic adaptive dosage adjustment technology to generate an optimized anesthetic scheme; the method comprises the following steps: extracting continuous physiological state parameters of a patient by applying an adaptive feedback control algorithm, presetting a safety threshold value, performing real-time comparison and analysis through a real-time parameter adjustment mechanism, performing automatic early warning processing on a trend deviating from a normal range by adopting an abnormal trend early warning technology, and generating a real-time adjustment instruction; and based on the real-time adjustment instruction, obtaining a patient recovery trajectory and subjective comfort evaluation, performing retrospective analysis, and generating a personalized anesthesia management scheme. According to the technical scheme provided by the invention, the safety of personalized anesthesia is remarkably improved.

The invention relates to the technical field of intelligent edge computing, and discloses a heterogeneous data processing optimization system based on intelligent edge computing. Comprising a heterogeneous data acquisition module, an edge preprocessing module, an intelligent optimization scheduling module, a heterogeneous fusion analysis module and a self-adaptive feedback control module. According to the system, data streams of different formats and protocols can be collected from a plurality of heterogeneous data sources in real time, and multi-modal correlation analysis is carried out after preprocessing and intelligent scheduling. Meanwhile, the system performance is optimized through self-adaptive feedback control, and a resource efficiency evaluation module and a safety compliance verification module are further arranged. According to the method, the problems of delay, accuracy, resource allocation, safety compliance and the like in heterogeneous data processing are effectively solved, the data processing efficiency and the system stability are improved, and the method is suitable for heterogeneous data processing scenes in the fields of industry, traffic, medical treatment and the like.

The invention provides a space-time intelligent ocean satellite internet of things perception information screening method and device, and belongs to the technical field of data processing. Marine environment data space-time prediction is carried out based on a block time sequence converter, marine hot spot area mining is carried out based on a density clustering algorithm of mean shift, and marine environment data comprises sensing data of sea surface temperature, salinity, wave height and the like; marine environment risk assessment is carried out based on a BP neural network, information screening feedback control is carried out based on multi-agent reinforcement learning, the efficient information processing requirement of a complex marine environment detection scene is met, and the low-redundancy and high-precision data acquisition and transmission adaptive feedback control effect is achieved. The method can be directly applied to an ocean observation and detection facility networking system in the ocean Internet of Things, can provide powerful support in the aspects of military application and civil application, and has wide and important application prospects and value.

The invention relates to the technical field of motor control, and discloses a low-noise flux linkage adaptive feedback control system of a high-speed motor. The system comprises an acquisition module used for acquiring current, voltage and vibration data of a high-speed motor at various operation rotating speeds to obtain operation parameters and noise characteristic data of the high-speed motor; the transformation processing module is used for performing frequency domain orthogonal transformation processing on the operation parameters and the noise characteristic data of the high-speed motor to obtain flux linkage real-time observation data; the self-adaptive analysis module is used for executing multi-mode working condition self-adaptive analysis according to the flux linkage real-time observation data and generating flux linkage control reference values of different rotating speed intervals; and the output module is used for inputting the flux linkage control reference values of different rotating speed intervals into a magnetic-machine coupling control algorithm, and calculating and outputting a motor low-noise driving control signal. According to the method, the problem of magnetic flux path distortion under high-speed operation is effectively solved, and downtime and efficiency loss caused by a traditional off-line calibration method can be avoided.

The invention relates to the technical field of three-step fine polishing methods for 19nm wafer regeneration, discloses a fine polishing method named'three-step fine polishing method for 19nm wafer regeneration ', and aims to solve the technical contradiction between macroscopic planarization and microscopic surface integrity of regenerated wafers in advanced process nodes. The method is characterized by comprising a first fine polishing step, a second fine polishing step and a third fine polishing step, and in each step, gradient decreasing polishing pad hardness, abrasive particle size and concentration, polishing pressure and pH value are adopted; and an on-line process monitoring and self-adaptive feedback control subsystem is used for assistance. By adopting the technical scheme, defects can be efficiently removed, damage can be repaired step by step, the wafer surface with ultralow defect density and excellent chemical stability is obtained, the requirements of the manufacturing process of 19nm and more advanced semiconductors are met, and the yield is increased.

The invention provides a pressure test control method and system for a universal pressure testing machine, and relates to the technical field of material testing machines, and the method comprises the steps: obtaining hydraulic cylinder parameters and servo valve parameters, building a pressure control mathematical model, calculating a transfer function, initializing a feedback controller, determining the number of hidden layer nodes, and setting an activation function and a network weight. A self-adaptive feedback controller is obtained; generating a closed-loop control system, constructing an objective function based on an integral absolute error and adjustment time, determining a Pareto optimal solution set in combination with a multi-objective genetic algorithm, selecting a neural network training sample, generating a training sample set, performing offline pre-training, and updating a network weight to obtain a pre-trained neural network; and the feedback controller is embedded into a pressure control system, outputs feedback controller parameters, sets an anti-saturation strategy of the feedback controller, judges whether the feedback controller is saturated or not, limits the amplitude of an integral item according to the relation between an error signal and a saturation direction, determines whether to stop integration or not, and generates a press control strategy under the current working condition.

The invention relates to the technical field of cargo unloading control technologies, and discloses a control system and method for a cargo unloading robot of a van truck. A van truck cargo unloading robot control system comprises an acquisition module, a main control module, an execution module and a light supplementing module. A control method of a van truck cargo unloading robot comprises the steps that multi-mode three-dimensional point clouds of a truck carriage and cargoes are obtained, the multi-mode three-dimensional point clouds are preprocessed, and ordered three-dimensional point clouds are obtained; performing path planning on the ordered three-dimensional point cloud through improved RRT * to obtain an initial path trajectory; optimizing the initial path trajectory to generate a predicted path trajectory; and based on the predicted path trajectory, data analysis is carried out through adaptive feedback PID control, and optimal control parameters of the mechanical arm are obtained. The unloading efficiency is effectively improved.

The invention belongs to the technical field of Internet of Vehicles control, and discloses an Internet of Vehicles channel congestion distributed closed-loop control method. According to the method, a closed-loop feedback channel resource congestion control strategy is provided, and a closed-loop feedback control framework adopted by the strategy is composed of reference model construction and adaptive feedback control model construction. And constructing a data packet acceptance rate model and a network effect maximization model in the reference model, and solving an optimal data packet reception probability as an ideal state of the Internet of Vehicles. In the adaptive closed-loop feedback control model, an error between an ideal state and an actual network state of the Internet of Vehicles is adjusted through a PI controller, so that the actual network state infinitely approaches the ideal network state. Network parameters are dynamically adjusted based on model reference adaptive control, the reliability of communication between vehicles in a dynamic network environment is ensured, and expected stability and robustness of a system can be kept in a dynamically changing environment.

The invention discloses a tennis racket batting data monitoring system based on an intelligent sensor, and particularly relates to a training data monitoring technology, which comprises a four-layer collaborative architecture, an intelligent tennis racket, an intelligent bracelet and a communication module, the four-layer collaborative architecture is composed of a sensor layer, an unmanned aerial vehicle collaborative layer, an edge computing layer and a cloud / user layer; a first communication module is arranged in the unmanned aerial vehicle cooperation layer and used for receiving racket data and environment perception, and the unmanned aerial vehicle cooperation layer communicates with the sensor layer in real time. According to the tennis racket ball hitting data monitoring system based on the intelligent sensor, comprehensive monitoring and analysis of tennis ball hitting data are achieved, the system can capture detailed information at the ball hitting moment, and an accurate ball hitting quality score is generated in combination with physiological data and environmental factors of players; scientific training feedback and competition strategy support are provided for players; the self-adaptive feedback control function of the system can dynamically adjust the training intensity according to the physical state of the player, and the training effect is effectively improved.

The invention discloses an electric toothbrush injection molding intelligent monitoring method and system based on sensing feedback, and relates to the technical field of intelligent monitoring. The method comprises the following steps: carrying out node region division on an electric toothbrush injection molding machine to obtain N injection molding process node regions, and deploying a multi-modal sensor group; injection molding process data streams of N nodes are collected and accessed to an RS485 communication network to perform data communication transmission between the multi-mode sensor group and the injection molding control center; the injection molding control center carries out molding prediction simulation and abnormal defect identification, and injection molding abnormal defect parameters are determined; and injection molding self-adaptive monitoring feedback is carried out, injection molding abnormal feedback parameters are obtained, and molding optimization control is carried out. The technical problem that in the prior art, the injection molding quality of the electric toothbrush is unstable is solved, intelligent recognition and self-adaptive feedback control over abnormal molding are achieved by conducting multi-mode sensing monitoring on the injection molding process, and the technical effect of improving the stability of the injection molding quality is achieved.

The invention belongs to the technical field of computer vision and intelligent sensing, particularly relates to a positioning system for monitoring blind area multi-sensor space-time calibration, and aims to solve the problems of monitoring blind areas and track breakage caused by non-uniform space-time reference of multi-source heterogeneous sensors. The system comprises a space-time reference generation unit, distributed sensing nodes, a joint calibration engine, a real-time positioning calculation module and a self-adaptive feedback controller. Through nanosecond time synchronization, space registration based on a static landmark and a mobile calibration carrier and a dynamic re-calibration mechanism of closed loop feedback, continuous smooth trajectory output of cross-device target tracking is realized. The system supports layered architecture and block chain evidence storage, improves expandability and security, and is suitable for smart cities and industrial automation scenes.

The invention relates to the technical field of data security, and discloses a data crushing method based on a four-dimensional manifold chaotic power system, and the method comprises the steps: generating a time-space coupled chaotic entropy source through system initialization, dynamically distributing entropy decomposition channels for input data according to type characteristics, and achieving the irreversible crushing and entropy control of the data; and then fragment-level distributed verification is cooperatively completed by means of edge nodes, an on-chain certificate with auditing performance is generated, real-time closed-loop updating is performed on chaotic parameters in combination with a verification result, and a self-adaptive feedback control mechanism is formed. According to the method, a Merkle-Patricia tree structure is improved to realize non-tampering and chained backtracking of data, a security signature based on a lattice structure is adopted to ensure the long-term effectiveness of verification information, and the stability of an entropy source is optimized through a chaotic disturbance feedback mechanism, so that the security, reliability and adaptability of a system under the threat of a high-complexity calculation model are comprehensively improved.

The invention discloses a modeling and closed-loop displacement control method for a shape memory alloy driver, and the method comprises the steps: taking an inverse hysteresis feed-forward compensation and SMC-PID feedback displacement control method as a core, building a positive model of the shape memory alloy driver, carrying out the inversion to obtain an inverse model, designing a self-adaptive SMC-PID feedback controller, and carrying out the closed-loop displacement control of the shape memory alloy driver. Combining the positive model, the inverse model and an SMC-PID feedback controller to construct a control system for closed-loop displacement control of the shape memory alloy driver; matlab / Simulink software is used for carrying out modeling simulation testing on the designed control system, and the closed-loop displacement control effect of the shape memory alloy driver is observed by selecting different tracking signals. The modeling and closed-loop displacement control method for the shape memory alloy driver has an excellent control effect under various motion working conditions such as back-and-forth reciprocation, continuous heating deformation and continuous cooling deformation of the shape memory alloy driver, is high in tracking precision and small in delay, and realizes high robustness for system delay and parameter uncertainty.

The invention relates to the technical field of industrial intelligent control and predictive maintenance, in particular to a multivariable time sequence prediction and closed-loop control method based on digital twinning and deep learning fusion, which comprises the following steps: constructing a multi-level data architecture based on a digital twinning five-dimensional model; data preprocessing and SMOTE resampling are carried out; time sequence feature extraction based on the 1D-CNN; virtual twin data generation based on Bi-LSTM (Bidirectional Long Short Term Memory) and an attention mechanism; performing multi-mode fault diagnosis based on the RNN-LSTM hybrid network; carrying out adaptive feedback control based on Lyapunov stability; a multi-task learning mechanism is adopted, and virtual twin data generation and fault diagnosis are jointly optimized. According to the method, excellent prediction precision and control performance are verified on a semiconductor etching process data set, and the fault diagnosis effect and the autonomous optimization capability of the system in the complex industrial process are remarkably improved.

This invention belongs to the field of multi-objective optimization technology, specifically involving a trend-guided dynamic multi-objective optimization evolutionary method, including: S1. Trend modeling and direction construction in intelligent scenarios; S2. Construction of perturbation and search mechanisms under trend guidance; S3. Multi-objective knee point identification and feedback reinforcement mechanism; S4. Evolutionary computation parameter control mechanism; S5. Elite sparse resampling and trend collaborative scheduling optimization. The advantages of this invention are: it constructs a closed-loop optimization system that integrates trend prediction, knee point reinforcement, perturbation generation, and adaptive feedback control, and achieves a comprehensive improvement in the convergence, directionality, diversity, and stability of the solution set in a dynamic environment, significantly enhancing the search directionality and global convergence speed, avoiding the inefficiency problem caused by relying on random perturbations, and helping to quickly locate high-quality optimal solution sets.

The invention discloses a motion analysis and feedback method and device and a computer readable storage medium, and the method comprises the steps: building a neutral position reference model based on the skeleton features of a user, and calculating the instantaneous deviation features of a real-time posture and the reference model; resolving a time domain accumulation feature representing the stubborn degree of the action error and a time domain change rate feature representing the deterioration trend of the action error in real time; retrieving the kinematic chain knowledge graph to obtain attribution information; inputting the features into an adaptive feedback control model, dynamically calculating abstract hierarchy parameters of a teaching strategy based on the magnitude of time domain accumulation features, respectively calculating mood specificity parameters and intervention opportunity parameters in combination with instantaneous deviation features and time domain change rate features, and generating prompt control parameters; and finally, system prompts are assembled based on the prompt control parameters, and self-adaptive feedback information is generated. According to the application, the crossing from mechanical error correction to adaptive closed-loop teaching can be realized.

The invention discloses a self-adaptive feedback regulation and control system and method for a teleprompter, and relates to the technical field of big data analysis, and aims to ensure that the importance of related contents is evaluated only when a user concentrates attention by accurately identifying the effective gaze of the user on the teleprompter and combining data such as a sight line deviation angle and gaze time, so that the user experience is improved. The distribution of information display resources is improved, excessive display of skilled information is avoided, and the interactivity and the intelligent level of the teleprompter are improved. Meanwhile, the importance degree of the display content is calculated, the content concerned by the user is dynamically screened and preferentially highlighted in combination with the content similarity, it is ensured that key information is displayed in real time, and the efficiency and practicability of the teleprompter are improved. The display progress is dynamically monitored in combination with the voice recognition technology, the display rhythm is accurately adjusted, it is ensured that the most concerned content of the user is displayed at the appropriate moment, the display process is optimized, and the application value and user experience of the teleprompter are improved.

The invention discloses a water quality sampling control method and control system coping with environment detection, and relates to the technical field of water quality sampling control. The control method comprises the steps that a system state vector is constructed, and a collaborative operation model cluster is adopted and loaded; wherein the collaborative operation model cluster at least comprises a state identification classification model, a sampling time prediction model, an improved resource optimization scheduling model and an adaptive feedback control model; the technical key points are as follows: dynamic adaptation is realized through multi-model cooperation, the problem that fixed logic cannot cope with a dynamic environment is solved, intelligent and self-adaptive operation of water quality sampling control is realized, and through linkage and cooperation of all models in a collaborative operation model cluster, the water quality sampling efficiency is improved. The operation strategy of water quality sampling work can be automatically adjusted under the complex working conditions of power failure, low power, water pressure fluctuation and the like, and task integrity and data representativeness are ensured.

This invention relates to a robot multi-joint motion deviation feedback control system and method based on temporal CNN, belonging to the field of robot motion intelligent control technology. The system includes a multi-source temporal data acquisition module, a temporal data preprocessing module, a temporal CNN spatiotemporal feature extraction module, a multi-dimensional motion deviation calculation module, a hierarchical adaptive feedback control module, a robot motion execution drive module, and a standard motion calibration and storage module. The method includes steps such as standard motion temporal calibration modeling, real-time temporal data acquisition and preprocessing, temporal CNN spatiotemporal feature extraction, multi-dimensional motion deviation calculation, hierarchical feedback command generation, motion closed-loop correction, and cyclic regulation. By employing temporal CNN to extract both spatial and temporal features of the motion, combined with hierarchical feedback and temporal prediction mechanisms, the invention overcomes the shortcomings of traditional control methods, such as feedback lag and inaccurate deviation identification, thereby improving the robot's multi-joint motion control accuracy, response speed, and environmental adaptability.

The application provides a kind of spatio-temporal intelligent marine satellite internet of things sensing information screening method and device, belong to data processing technical field, this method is based on convolution bidirectional long short-term memory neural network realizes marine environment time series data repair, based on block time series transformer carries out marine environment data space-time prediction and based on mean shift density clustering algorithm carries out marine hotspot area mining, the marine environment data includes sea surface temperature, salinity, wave height and other sensing data, based on BP neural network carries out marine environment risk assessment and multi-agent reinforcement learning carries out information screening feedback control, meet the complex marine environment detection scene efficient information processing demand, realize low redundancy, high-precision data acquisition transmission adaptive feedback control effect.The present application can be directly applied to marine internet of things in marine observation detection facility networking system, can provide strong support in military application and civil application, has wide and important application prospect and value.

This invention discloses an adaptive compensation control method for uneven settlement in soft strata, belonging to the field of settlement control in geotechnical engineering. The method involves collecting distributed micro-strain field and pore water pressure gradient data at the bottom of the foundation in the soft strata and inputting them into a stratum rheological evolution prediction model. Multi-scale spatiotemporal features are extracted using a spatiotemporal attention coding module to generate a stratum creep trend vector. The equivalent modulus correction coefficient is calculated using a virtual stiffness reconstruction algorithm to generate a dynamic stress wave excitation parameter set. Coherent stress beams are emitted using microarray actuators embedded in the bearing layer interface, and a dynamic stress-locking zone is constructed through constructive interference to form a virtual rigid support. The excitation parameters are corrected through a dual-loop adaptive feedback control mechanism combined with a heuristic optimization algorithm until the micro-strain field returns to uniformity. Simultaneously, energy dissipation monitoring and safety mechanisms are added to mitigate the risk of soil liquefaction. This invention achieves active suppression and adaptive compensation for uneven settlement in soft strata, improving the accuracy of settlement control.

The invention provides an electronic dobby detection control method, system and equipment, and relates to the technical field of intelligent control of textile machinery, and the method comprises the steps: collecting a multi-source monitoring data set in the operation process of an electronic dobby shedding device; performing on-off state detection on the electromagnetic driving unit of each electronic dobby in sequence to generate an electromagnetic abnormality diagnosis result; comparing the action time sequence data on the basis of a preset movement track, and generating an abnormal action judgment result of each electronic dobby; performing multi-dimensional abnormal correlation analysis in combination with oil level information of the hydraulic system, and constructing a multi-factor coupling state evolution chain; and carrying out abnormal hierarchical positioning, generating a self-adaptive adjustment strategy, and carrying out self-adaptive feedback control. The technical problems that in the prior art, electronic multi-arm detection mostly depends on single-type signal detection, the running state of equipment cannot be evaluated timely and comprehensively, an effective diagnosis and early warning mechanism for equipment abnormity is lacked, and then the stability and production efficiency of the equipment are difficult to guarantee are solved.

The present invention discloses a two-degree-of-freedom active suppression device and method applied to a mechanical vibration system. The device includes a vibration active control data acquisition and operation part, a power amplification part, and a vibration active control actuator; the method is as follows: the control software adopted is a core control program based on an adaptive feedback control algorithm built on an FPGA and a vibration active measurement and control software; the core software realizes the calculation of the driving signal of the actuator through the control algorithm, and the vibration active measurement and control software completes functions such as vibration signal detection, test process debugging, and communication coordination control; compared with the existing vibration control technology, the present invention has no complex and cumbersome mechanical actuators, has a simple structure and a fast response speed, and can perform real-time high-precision control through the signal provided by the control system; compared with the existing vibration control technology, the control system of the present invention adopts a filter adaptive control algorithm and digital signal processing technology, has high sensitivity, and the control process is reliable and stable.

This invention relates to the field of UAV spraying and discloses an adaptive compensation method, system, device, and medium for live-line spraying of anti-flashover coatings on UAVs. The method includes establishing a flight reference coordinate system and a dynamic model of the UAV's flight attitude under disturbance; analyzing the external environmental loads and internal mass changes of the UAV based on the dynamic model to obtain the total disturbance; constructing an adaptive feedback control network to obtain a compensation control quantity to offset the impact of the total disturbance on the UAV's flight; and dynamically updating the controller gain and the adaptive feedback control network based on the real-time estimated disturbance state to achieve active disturbance rejection flight control. This invention dynamically updates the controller gain and provides feedback compensation for disturbances caused by external environmental load disturbances and changes in the UAV's internal mass, thereby achieving active disturbance rejection and adaptively adjusting flight stability, ensuring the safety of live-line spraying of anti-flashover coatings on UAVs.

The application discloses a method and system for intelligent monitoring of injection molding of an electric toothbrush based on sensing feedback, and relates to the technical field of intelligent monitoring. The method comprises the following steps: dividing node regions of an injection molding machine of the electric toothbrush to obtain N injection molding process node regions, and deploying a multi-modal sensor group; collecting N node injection molding process data streams, and transmitting data between the multi-modal sensor group and an injection molding control center through RS485 communication network; performing molding prediction simulation and abnormal defect identification by the injection molding control center to determine injection molding abnormal defect parameters; performing injection molding adaptive monitoring feedback to obtain injection molding abnormal feedback parameters, and performing molding optimization control. The technical problem of unstable quality of injection molding of the electric toothbrush in the prior art is solved, intelligent identification and adaptive feedback control of molding abnormalities are realized through multi-modal sensing monitoring of the injection molding process, and the technical effect of improving the stability of the injection molding quality is achieved.

This invention provides a connected vehicle cooperative control method based on adaptive feedback technology, comprising the following steps: establishing a simplified vehicle queuing control model; acquiring the actual control input of each following vehicle while considering controller gain variations; adding an adaptive feedback controller; optimizing the adaptive feedback controller to calculate tracking error while considering packet loss in inter-vehicle communication data; and transforming the vehicle queuing control model to achieve vehicle queuing control. This invention proposes a distributed adaptive feedback controller for vehicle queuing that guarantees both internal stability and L2 queue stability, wherein the feedback gain and parameter adaptive law of each following vehicle are dynamically determined based on its own tracking error. This avoids the packet loss matrix mentioned earlier, significantly reducing computational complexity.