48 results about "Adaptive feedback control" patented technology

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 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 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 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 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.

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.

The invention discloses a credential data transmission secrecy method based on self-adaptive dynamic authority control. The method comprises the following steps: S1, constructing a topological structure of a data transmission network, and outputting a preliminary scheme of a dependency relationship and an encryption strategy; s2, generating a dynamically adjusted encryption and authority control strategy combination through a causal inference module; s3, the encryption and authority control strategy combination is input into a perturbation analysis module, small-range strategy perturbation is carried out, and an optimized strategy result is output; s4, inputting an optimized strategy result into a space-time adaptive prediction model based on meta-learning, and obtaining a foresight optimization strategy; s5, inputting the prospective optimization strategy into an adaptive feedback control module, and outputting a dynamic optimization real-time strategy; s6, inputting the dynamic optimization real-time strategy to a network flow state real-time monitoring module, and optimizing an encryption and authority control strategy; and S7, forming closed-loop adaptive regulation and control. According to the method, dynamic encryption and authority control are realized by adopting network topology, causal inference and meta-learning.

This optronic sight for a motorized vehicle, such as an aircraft, marine vehicle, or land vehicle, comprises: a sighting module (13) capable of being moved around a first axis (X) and a second axis (Y) not parallel to the first axis; means (14, 15) for moving the sighting module around the first and second axes; and a feedback loop for controlling a position parameter of the sighting module based on a setpoint, comprising an adaptive feedback controller (16) capable of calculating a command for a controlled device having a critical mode that modifies the device's response. It includes means (23) capable of determining a fundamental frequency of the critical mode that varies according to an operating parameter of the controlled device, the controller (16) being calculated from the fundamental frequency determined to compensate for the anti-resonance or resonance mode. Abstract figure: Figure 5

The invention relates to the field of preparation of high-purity copper, in particular to a method for reinforced leaching of impurities in complex raw materials through methanesulfonic acid, and particularly relates to a reinforced leaching method of high-purity copper, which comprises the following steps: introducing methanesulfonic acid into a leaching solution as an auxiliary agent, and increasing the dissolution rate of insoluble impurities in combination with a complex reaction to obtain the high-purity copper. And parameters such as the temperature, the pH value and the stirring speed are optimized to strengthen the dynamic process. And meanwhile, the concentration change of copper ions is dynamically monitored, and the addition amount of methanesulfonic acid is adjusted by adopting a self-adaptive feedback control algorithm, so that accurate regulation and control are realized. In addition, impurities are separated in a gradient mode through a multi-stage filtering system, conductivity and impurity residues are evaluated in combination with an electrochemical impedance spectrum, technological parameters are dynamically optimized, and follow-up electrolysis energy consumption is reduced. According to the method, the impurity leaching efficiency can be remarkably improved, the refining burden is reduced, the high-end application requirement is met, economical efficiency and environment friendliness are achieved, and a new scheme is provided for sustainable development of the industry.

The application discloses a kind of shape memory alloy driver's modeling and closed loop displacement control method, this method with inverse hysteresis feedforward compensation and SMC-PID feedback displacement control method as core, the positive model of shape memory alloy driver is established, and inverse model is obtained by inversion, adaptive SMC-PID feedback controller is designed, the positive model, inverse model and SMC-PID feedback controller are combined, and the control system for the closed loop displacement control of shape memory alloy driver is constructed;The control system designed is modeled and simulated test using Matlab / Simulink software, by selecting different tracking signals, observe the closed loop displacement control effect of shape memory alloy driver.Shape memory alloy driver's modeling and closed loop displacement control method of the application has excellent control effect under shape memory alloy driver back and forth, continuous heating deformation, continuous cooling deformation and other various motion conditions, with high tracking accuracy, small delay, realizes the strong robustness of system hysteresis and parameter uncertainty.

An optronic sight for a motorized vehicle such as an aerial or marine vehicle propelled by a propeller, or a tracked land vehicle. The optronic sight can include an aiming module, means for moving the aiming module about the first and second axes, and means for continuously measuring angular data. The optronic sight can further include a feedback control loop having means for continuously measuring the acceleration of the aiming module in three orthogonal directions of the space, means for detecting at least one fundamental frequency of the vibratory disturbances, and an adaptive corrector configured to continuously receive as input said fundamental frequency, a discrepancy between an angular setpoint value and said angular data, output a movement setpoint value to the moving means.

The invention discloses a GaN-based nano LED array etching method based on optical feedback. The GaN-based nano LED array etching method comprises the following steps: firstly, obtaining a native luminous efficiency distribution diagram of a GaN epitaxial wafer through cathode fluorescence scanning to establish a reference; after the hard mask of the nanoscale array pattern is formed, the epitaxial wafer is arranged in etching equipment provided with a laser light source and a spectrum detector; in the etching process, photoluminescence signals, including spectrum peak positions and luminous intensity, of an etching area are excited and collected in real time; calculating real-time relative luminous intensity in combination with the native luminous efficiency distribution diagram; and finally, dynamically adjusting bias power and passivation gas flow according to the attenuation rate of the relative luminous intensity, and judging that etching is completed when blue shift of a spectrum peak position stops. According to the invention, in-situ optical monitoring and self-adaptive feedback control in the etching process are realized, plasma etching damage can be effectively reduced, and the etching uniformity of the nano LED array and the quantum efficiency of the device are improved.

Methods and systems that use adaptive feedback control to control the in vivo concentrations of target molecules or ions accurately and rapidly by estimating the pharmacokinetics of the individual subject at site of control are described. The systems can estimate the pharmacokinetics of the molecules or ions under control without prior pharmacokinetic knowledge of the subject's specific physiology or the pharmacokinetics of the molecules or ions under control, ensuring that the resulting control is rapid and accurate. The adaptive feedback control systems can rapidly achieve and accurately maintain a predefined concentration in the tissue that is the site of action of the molecules or ions.

The application discloses a kind of high dynamic mass eccentric self-adaptive control method and system without rudder surface, belong to the field of guidance and control technology, this method is by real-time sensing aircraft state, combined with feedforward interference compensation and adaptive feedback control, drive internal eccentric mass block to realize micron-level accurate positioning, to generate accurate control moment without the need for exposed rudder surface.System includes two-dimensional eccentric mass execution system, non-contact position sensing system and intelligent control and processing unit, with fast response, high precision, full airspace application, good stealth, etc.Advantages, suitable for high-precision attitude control in various high-demand civil and scientific research tasks of high-speed rotating aircraft.

The invention discloses a blast furnace gas utilization rate time sequence judgment and self-adaptive decision making system and method, and the system comprises a blast furnace full ironmaking data processing module which is used for preparing a'material distribution-furnace feeding-operation-monitoring-test 'full-chain dynamic data set based on a computer technology and an ironmaking process theory; the blast furnace gas utilization rate prediction module is used for predicting the gas utilization rate by adopting a machine learning or data mining algorithm based on the historical operation data and the real-time data; the case library dynamic adjustment module is used for performing clustering analysis on historical data of key monitoring targets and matching fluctuation adjustment ranges of the monitoring targets under different coal gas utilization rate grades to form a case library; the self-adaptive feedback control module is used for setting a corresponding control strategy based on an output result of the prediction module; and the integration module is used for integrating the modules into a closed-loop control system to realize time sequence judgment and self-adaptive decision of the blast furnace gas utilization rate.

The application discloses a water quality sampling control method and control system for environmental detection, and relates to the technical field of water quality sampling control; the control method comprises the following steps: constructing a system state vector, and adopting a cooperative operation model cluster and loading; wherein the cooperative operation model cluster at least comprises a state recognition classification model, a sampling time prediction model, an improved resource optimization scheduling model and a self-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 dynamic environment is solved, intelligentization and self-adaptive operation of water quality sampling control are realized, and through linkage and cooperation of each model in the cooperative operation model cluster, water quality sampling work can independently adjust an operation strategy under complex working conditions such as power failure, low power and water pressure fluctuation, and task integrity and data representativeness are ensured.

The application discloses an intelligent online liquid-phase analysis system, which comprises an optical sensing unit and an intelligent control and processing unit, the optical sensing unit is used for emitting a light beam to a sample liquid and collecting original light intensity signals of transmitted light and at least two angle scattering lights and an original light source intensity signal, the intelligent control and processing unit comprises a data acquisition module, a data preprocessing module, a multi-source data fusion inversion module, a system health state self-diagnosis module and an adaptive feedback control module, the data acquisition module is connected with the optical sensing unit and is used for receiving the original light intensity signals and the original light source intensity signal and converting them into digital signals.The application has the beneficial effects that the problems of isolated data processing, low intelligentization, and inability to identify non-sample factor interference in the prior art are solved, and the accuracy, reliability and long-term operation stability of online liquid-phase analysis are greatly improved.

The application relates to the technical field of big data processing and distributed computing, in particular to a distributed big data batch processing optimization method based on SpringBatch, which comprises the following steps: multi-dimensional entropy analysis and fragmentation: performing random pre-sampling calculation key value information entropy, dynamically adjusting histogram bucket number, and establishing a dynamic fragmentation strategy based on a work-stealing mode; self-adaptive feedback control: collecting JVM heap memory usage, dynamically adjusting data grabbing size by using an incremental PID algorithm and a variable step limiter, and forming a self-adaptive back pressure mechanism; zero copy and reuse: performing zero copy conversion by using off-heap memory mapping, and reducing object creation frequency by using object pool reuse technology; asynchronous submission and fault tolerance: combining and submitting large transactions based on an asynchronous disk brushing strategy, and performing hierarchical retry operation; the application realizes statistical perception self-adaptive control of a batch processing process, and significantly improves system throughput and data consistency.

This invention relates to the field of smart grid technology, specifically a method and system for quantum time-frequency synchronization in distribution network areas. The method includes: deploying a master reference unit on the distribution transformer side to generate a high-precision reference time signal and distribute entangled photon pairs within the distribution area; deploying multiple slave node synchronization units on each smart terminal device to receive entangled photon pairs and record photon arrival times; calculating the real-time quantum clock difference between each slave node and the master reference unit using the coincidence counting principle; taming the local CPT atomic clock using an adaptive feedback control module based on the quantum clock difference to achieve time synchronization with the master reference unit; and monitoring the status of the distribution network communication network in real time, automatically switching between closed-loop control mode and open-loop hold mode based on the network status. This invention achieves highly reliable and accurate synchronization of distribution network areas without relying on the public network, possesses anti-spoofing attack capabilities, and provides a secure and accurate time reference for distribution network equipment.

The embodiment of the invention provides a wind power plant equivalent modeling method based on a self-adaptive multiplication module and related equipment, and belongs to the technical field of power system modeling. The method comprises the following steps: grouping wind power plant units; a single-unit multiplication equivalent model is constructed for each unit group, the core of the model is that a self-adaptive multiplication module is adopted, and a traditional RC damping unit with fixed parameters is replaced with a variable damping unit with adjustable parameters; a self-adaptive feedback control mechanism is introduced, output current of an equivalent unit is collected in real time and compared with target current, generated deviation signals are processed through a PI controller, parameters of a variable damping unit are dynamically adjusted, and the output current accurately tracks the target value. According to the method, the defects that a traditional multiplication equivalence method depends on manual setting of fixed parameters and cannot adapt to complex working conditions are overcome, automatic optimization and self-adaptive adjustment of the wind power plant equivalent model parameters are achieved, and the robustness of the model in different operation scenes is remarkably improved.