76 results about "Local linear" patented technology

The invention provides a network security monitoring method and system based on distributed nodes, and the method comprises the steps: obtaining an inter-node interaction record of each node in a distributed network in a preset communication period and the identification information in a protocol interaction process, and obtaining a node communication data set; the method comprises the following steps: constructing a multi-dimensional phase space reconstruction matrix containing communication time sequence association features and protocol identifier association features, carrying out nonlinear dynamic feature analysis on the matrix, extracting a chaotic feature value set of node communication behaviors, constructing a topological association structure of node communication features through a local linear relationship, mapping the set to a topological space of a preset dimension, and constructing a topological structure of the node communication behaviors. And generating an attack mode topology expression vector, and finally analyzing an abnormal state propagation process in the distributed network through an inter-node energy propagation rule based on the attack mode topology expression vector to obtain a network security detection result. According to the invention, the accuracy and dynamic analysis capability of distributed network security monitoring can be effectively improved.

The invention relates to the technical field of dam safety monitoring, and discloses a multi-source remote sensing data deep learning dam slope monitoring method, which comprises the following steps: resampling each mode to a common ground grid, and calculating robust statistics and a time stability agent on grid and block scales; determining a reference image according to the block-level robust score, and adaptively setting a local displacement search range with a high-intensity centroid difference; evaluating the discrete candidate displacement in a grid neighborhood by using a median absolute difference to obtain a local displacement field and a residual error; three types of subitems are constructed based on robust noise, time stability and registration residual errors, and pixel-level reliability weights are adaptively synthesized through logarithmic variance proportions among modes; analyzing and solving local linear mapping in a neighborhood by using a weight-weighted observation matrix, and calculating a weighted residual error according to the local linear mapping; a binary and probability anomaly graph is generated with a robust threshold.

The invention discloses a transformer abnormity identification method based on voiceprint feature analysis, and belongs to the field of power equipment state monitoring and intelligent diagnosis. The method comprises the following steps: firstly, analyzing an iron core acoustic mechanism based on a magnetostrictive effect, and establishing a three-dimensional model through finite element simulation to obtain vibration and sound field characteristics; in a complex substation environment, a hybrid noise reduction method combining density peak clustering and a CEEMDAN-wavelet threshold is provided, and the signal-to-noise ratio is effectively improved. Then extracting Mel-frequency cepstrum coefficients (MFCC) and spectrum features, and performing local linear embedding (LLE) dimension reduction to form a compact feature set; in the recognition stage, a convolutional neural network framework is designed, specifically, a spectrogram and an energy spectrum are modeled through a two-dimensional CNN, an MFCC tensor obtained after dimensionality reduction is modeled through a three-dimensional CNN, and accurate diagnosis of mechanical faults such as core looseness is achieved. The method has the advantages of being non-contact, anti-noise and high in recognition precision, and real-time diagnosis and early warning of mechanical abnormity of the transformer can be achieved under complex working conditions.

The invention relates to the field of fan spindle state monitoring, and aims at synchronously acquiring sound and vibration signals through multiple channels, realizing nanosecond time alignment by adopting a precision time protocol, and performing denoising and normalization preprocessing on the signals to improve data integration and signal fidelity. Furthermore, short-time Fourier transform and continuous wavelet transform are combined to extract multi-scale time-frequency features, and a high-dimensional combined feature vector is generated in combination with cross-correlation analysis. And mapping the feature vectors to a low-dimensional manifold space through a local linear embedding algorithm, and constructing a dynamic mode reference template. Indexes such as curvature, track length and direction entropy are monitored in real time, whether the spindle has an abnormal evolution trend or not is judged through a self-adaptive curvature threshold, and abnormity judgment is achieved in combination with track backtracking verification. According to the scheme, the abnormal starting boundary of the spindle state can be caught in a refined mode, and the early warning and operation and maintenance response capacity of fan operation is effectively improved.

The invention relates to the technical field of computer-aided engineering, and discloses an intelligent water conservancy design simulation system based on digital twinning, which comprises a parameterized geometric feature extraction module, a reduced-order model database, a tangent space linear deduction module and a flow field reconstruction module. According to the method, nonlinear flow field solution is dimensionally reduced into deterministic matrix vector multiplication, traditional statistical regression and iterative solution are replaced, it is ensured that a reconstruction result evolves in the tangential direction of a physical gradient, non-physical guess of a statistical model in a sparse sample area is avoided, and the calculation accuracy is improved. And high-fidelity flow field reconstruction with real-time performance and physical evolution conservation is realized.

The invention relates to a dynamic light beam shaping system and method based on a spatial light modulator. The system comprises a laser light source module, a beam expanding collimation light path, a light beam modulator, a focusing optical assembly, a detection and feedback module and a control and optimization module. According to the method, the uniformity of the output flat-topped beam can be corrected by virtue of a random parallel gradient descent (SPGD) algorithm and combining a Zernike polynomial as a control variable. Meanwhile, a composite performance index comprehensively considering energy distribution and shape features is constructed, the correction process of the flat-topped beam is accelerated by adopting a mode of gradually improving Zernike polynomial orders in multiple stages, the convergence trend of the performance index is used as a judgment basis of stage switching, stage independent variable dimension expansion is implemented, and the correction precision of the flat-topped beam is improved. And the convergence speed is obviously improved. Besides, adaptive selection of disturbance amplitude and learning rate parameters in the SPGD algorithm is realized by using noise estimation and a local linearity index, and the robustness of the system to noise and environmental disturbance is enhanced.

The invention provides a lithium battery cargo abnormal temperature rise identification method, and belongs to the technical field of lithium battery customs detection.The lithium battery cargo abnormal temperature rise identification method comprises the steps that a temperature sensor array is arranged on the surface of a lithium battery cargo stacking body to collect multi-point temperature time sequence data, and trend characteristics are extracted through wavelet packet decomposition denoising and singular spectrum analysis; establishing a state space model based on a heat conduction partial differential equation, estimating an internal temperature field state by using Kalman filtering, solving a heat conduction inverse problem by using a conjugate gradient regularization iterative algorithm to invert an internal three-dimensional temperature distribution field, and inputting an inversion result and statistical characteristics into a thermal anomaly identification model fused with manifold learning. Dimensionality reduction is carried out through a local linear embedding algorithm, a mahalanobis distance is calculated in a low-dimensional manifold space, an abnormal temperature rise risk score is output, when the score exceeds a preset threshold value, early warning is triggered, and the technical problem that the abnormal temperature rise in the lithium battery cargo stacking body is difficult to accurately recognize through surface temperature measurement is solved.

The invention discloses a multi-angle laser radar vertical two-dimensional wind field inversion method. The method comprises the following steps: firstly, setting a measurement center and sampling parameters of a measurement area; acquiring multi-angle radial wind speed data; introducing a multi-point local sampling strategy to each inversion center point, and establishing a local multi-constraint inversion equation set by selecting a plurality of observation points close to the spatial position of the center point and combining the observation angle and radial wind speed information; according to the method, the limitation that a traditional wind field inversion method depends on a large-range overall consistency hypothesis is broken through, a local linear modeling method is adopted, small-scale changes, local non-uniformity and turbulence disturbance existing in an actual wind field can be better adapted, and the adaptability and modeling precision of an inversion model to a complex wind field structure are improved; the anti-noise capability, the error control and the inversion convergence are remarkably improved; the invention further provides a system, equipment and a storage medium for implementing the method.

The invention provides an efficient data acquisition method and system for an HPLC (High Performance Liquid Chromatography) dual-mode communication module based on edge calculation, and relates to the technical field of HPLC dual-mode communication data acquisition. The method comprises the steps of collecting multi-dimensional resource data of an HPLC dual-mode communication module in real time, performing feature extraction by adopting a local linear embedding algorithm, constructing a resource state evaluation model based on an extreme learning machine, evaluating a resource load state of the communication module in real time, and establishing a multi-target optimization model. Solving by adopting a non-dominated sorting genetic algorithm III to obtain an acquisition strategy solution set, selecting an optimal acquisition strategy, converting the optimal acquisition strategy into a control instruction, and adjusting data acquisition parameters in combination with predictive control and a feedback correction mechanism. According to the method, the problems of resource waste, single index and the like of an existing scheme are effectively solved by performing adaptive denoising, feature extraction and resource state accurate evaluation on the multi-dimensional data and combining multi-objective optimization of data space-time association mining and acquisition efficiency, energy consumption and reliability.

This application discloses an artificial intelligence-based method, apparatus, device, and medium for 3D cell structure reconstruction, relating to the field of cell reconstruction. The method includes determining cross-modal fusion features using a multi-scale feature encoder based on point cloud data in the same coordinate system and pre-defined biological prior features, including local feature extraction, global feature extraction based on multi-head relative self-attention, and cross-modal feature fusion; performing dimensionality reduction using an improved local linear embedding manifold learning algorithm based on the cross-modal fusion features; determining topology-enhanced high-dimensional features using a graph convolutional network based on spatial proximity, feature similarity, and biological relevance, using point cloud data in the same coordinate system, pre-defined biological prior features, cross-modal fusion features, and low-dimensional features preserving topological relationships; and performing 3D reconstruction using a decoder based on the topology-enhanced high-dimensional features. This application can improve the accuracy and mechanistic interpretability of 3D cell models.

The invention relates to the technical field of power monitoring, and discloses a power module operation state monitoring method and system, and the method comprises the steps: carrying out the multi-frequency calibration of a high-frequency electrical signal of a power module, and obtaining a standardized multi-dimensional electrical signal; decomposing the standardized multi-dimensional electrical signal into a limited bandwidth modal component, and performing Hilbert transform to obtain a high-dimensional state feature vector; performing local linear embedding dimension reduction on the high-dimensional state feature vector to obtain a low-dimensional reference coordinate; acquiring a low-dimensional monitoring coordinate of the current high-dimensional state feature vector; constructing a decision diagram of the low-dimensional reference coordinates, and performing clustering center-to-center spacing analysis on boundary points in the decision diagram to obtain a dynamic adaptive threshold value; state deviation degree analysis is carried out on the power module, difference comparison is carried out on an analyzed state abnormity index and a dynamic self-adaptive threshold value, and a state abnormity early warning signal of the power module is generated; according to the invention, the efficiency of monitoring the running state of the power module can be improved.

The application discloses a non-rigid point cloud registration method and relates to the technical field of three-dimensional reconstruction. 2 The application further discloses a non-rigid point cloud registration method, which comprises the following steps: standardizing data points of a collected non-rigid point cloud and reference point cloud; calculating a local linear embedding weight matrix L of the data points; calculating a matrix M of the data points according to the matrix L; calculating a Gram matrix G of the data points; calculating a corresponding matrix P of the data points based on the reference point cloud; iteratively calculating a non-rigid transformation coefficient matrix W by using an L-M algorithm based on the matrix G, the matrix M and the matrix P; updating λ and σ in the non-rigid transformation coefficient matrix W according to the number of iterations; calculating the matrix P and the non-rigid transformation coefficient matrix W again; setting an iteration termination condition, that is, the number of iterations reaches a set value or a relative error of a target function value is less than a threshold value; and outputting a non-rigid transformation T based on the matrix G and the non-rigid transformation coefficient matrix W after the iteration is terminated.

The invention discloses a peak correction and characteristic scale standardization method for liquid chromatogram data. The method mainly comprises the following steps: acquiring chromatographic data of an experimental sample to be detected and preprocessing the chromatographic data; carrying out local linear transformation on the abscissa (X-axis) of each peak by adopting a local expansion and contraction method, and accurately aligning the peak center to the standard retention time; the peak information of each sample is corrected through Gaussian fitting, so that the change of the peak information before and after alignment is within a threshold range; and finally, carrying out X-axis standardization processing, unifying the time interval and the characteristic quantity of the samples, and comparing the data of each sample under the same scale. The method is used for eliminating the inconsistency of the chromatographic data caused by different instruments and experiment conditions, successfully correcting the peak information in the data and unifying the characteristic scale; therefore, the chromatographic data can be imported into the machine learning model more simply and accurately for further analysis.

The invention provides a robust graph comparative learning method and system for traffic adversarial attacks of the Internet of Vehicles. The method comprises the following steps: according to a pre-screening neighbor result of provided data and a one-dimensional structure entropy, calculating to obtain an optimal neighbor number of the provided data; on the basis of the optimal neighbor number, local linear reconstruction is used, neighbor weights are obtained through calculation, and a robust optimization graph structure is constructed; and according to the robust optimization graph structure, a difficult case mining method based on a double-memory bank is adopted, and through class prototype alignment of low-rank decomposition, reinforcement of robust embedding representation of the model is realized. According to the robust graph comparative learning method for the traffic adversarial attack of the Internet of Vehicles, provided by the invention, through a high-quality graph structure and GCL negative sample construction, the model robustness and the feature discrimination degree are balanced while the adversarial attack is resisted, so that in a complex Internet of Vehicles intrusion detection environment, the robustness of the model is greatly improved. And the robustness and the safety of the system are obviously improved.

The application discloses a method for intercepting a maneuvering target based on linear quadratic differential game proportional guidance, which comprises the following steps: obtaining a meeting state quantity and constructing a locally linearized relative lateral dynamic equation rate based on the two-dimensional relative motion relationship between a pursuit side and a target; describing the locally linearized relative lateral dynamic equation as a finite time domain zero-sum linear quadratic differential game, constructing a lateral deviation and lateral velocity approximate state vector according to the meeting state quantity, and solving an optimal lateral control quantity of the pursuit side; corresponding the optimal lateral control quantity of the pursuit side with a proportional guidance law, calculating an instruction angular velocity of the pursuit side, and realizing online interception of the target. The application can realize real-time adjustment of the heading angular velocity of a fixed-wing unmanned aerial vehicle, and improve the success rate of online interception of a target rotor-wing unmanned aerial vehicle. The application can be widely applied to the technical field of maneuvering target interception.

The application discloses a kind of based on ST-GAN's multi-source data dynamic risk early warning method and system, the method includes the following steps: S1. constructing the spatiotemporal generation confrontation network suitable for spatiotemporal data characteristics, utilize the network to generate extreme precipitation data, realize spatiotemporal unbalanced data reduction, obtain the equalization data of final output;S2. the equalization data of final output with high-dimensional space variable is carried out multi-source data fusion, and nonlinear spatiotemporal information conversion equation, and by local linearization obtains linear approximation model, to predict future time series;S3. under the present situation that extreme rainfall data amount is relatively insufficient, neural network based on dual learning theory accurately learns the parameter of nonlinear spatiotemporal conversion, estimates extreme weather event.The application is through spatiotemporal generation confrontation network (ST-GAN) and multi-source data fusion engine, significantly improves the precision and efficiency of natural disaster warning.

The present application relates to the field of strain detection, and particularly to a self-adaptive strain detection method, system and medium. The method comprises: inputting grid point coordinates, noisy displacement data and a maximum smoothing half window; calculating an overall noise level sigma through local linear regression analysis; extracting a local strain curvature characteristic quantity through cubic polynomial regression; establishing a noise and curvature balance relationship based on sigma and the curvature characteristic quantity, and calculating an adaptive smoothing half window; obtaining strain estimation values of each grid point through linear polynomial regression, and finally obtaining overall strain distribution. The method can automatically adjust the optimal smoothing window without manual intervention, balances between noise suppression and detail preservation, solves the problem of insufficient accuracy of traditional fixed window methods in non-uniform deformation areas, realizes high-precision full-field strain detection of noisy displacement fields, and is suitable for derivative calculation of digital image correlation systems and various noisy continuous signals.

The invention relates to a dynamic measurement method for a convective heat transfer coefficient on the surface of heat exchange equipment, which specifically comprises the following steps of: (a) drawing up discrete points of the convective heat transfer coefficient, constructing a plurality of local linear heat transfer models and calculating a sensitivity coefficient; (b) establishing a temperature prediction model based on the model and the sensitivity coefficient, and obtaining a temperature prediction value of the measuring point; (c) comparing the predicted value with the measured value through rolling optimization to obtain a fluid temperature compensation quantity component; (d) constructing a weighting coefficient of the local linear heat transfer model according to the fluid temperature estimation deviation; (e) carrying out weighted integration on the discrete points, and outputting a convective heat transfer coefficient measurement value at the current moment; and (f) circularly updating the time domain until the measurement is finished. According to the method, the nonlinear system is segmented through local linearization, and rolling optimization and weighting strategies are combined, so that the surface convective heat transfer coefficient can be quickly and accurately measured.

The present application belongs to the technical field of image recognition, and particularly relates to a plate environmental protection coating surface defect identification method and system based on machine vision, which comprises the following steps: constructing a structure tensor matrix and calculating a local linearity factor; inversely modulating an original gradient vector by using the local linearity factor to construct an inverse modulation flow field; calculating the divergence of the inverse modulation flow field to generate a divergence potential energy diagram, and extracting a suspected defect center according to the local extreme value of the divergence potential energy diagram; and constructing a phase entropy index of the suspected defect center, and comparing the phase entropy index with a bubble determination threshold to determine a bubble defect. The present application can effectively suppress the interference of a high linearity wood grain background, distinguish bubble defects from surface stains and dirt, and improve the accuracy and robustness of plate coating surface defect detection.

The invention discloses a hydraulic ring geological disaster intelligent identification method based on deep learning, and the method comprises the following steps: 1, collecting original data of a plurality of sensors, and carrying out the preprocessing of the original data, and generating the original data of a unified structure; 2, projecting the original data of the unified structure to a low-dimensional space through local linear embedding, and generating a spatial feature vector; 3, calculating a Manhattan distance between the spatial feature vectors to construct a cumulative distance matrix, obtaining an optimal alignment path, and generating a time sequence feature vector; 4, analyzing the persistent features of the time sequence and spatial features, and constructing a persistent feature map; 5, generating a multi-dimensional feature vector set through an improved Markov random field; and 6, performing anomaly detection according to the Gaussian mixture model, generating disaster early warning information, and performing visual output. According to the method, multi-sensor data and deep learning are fused, and the accuracy and real-time performance of hydraulic ring geological disaster early warning are improved.

The application belongs to the technical field of aero-engine control, and specifically discloses a VCE cross-mode control method which takes into account thrust rapid response and fuel economy, comprising the following steps: screening feasible steady points offline, and constructing a thrust-economy fuel guide mapping; calculating a reliability factor online according to the thrust-economy fuel guide mapping; obtaining a local linear prediction model according to current scheduling variable sampling, and constructing an output prediction matrix in a prediction time domain; constructing a model predictive control cost function and a hard constraint condition, and then solving a quadratic programming problem to obtain a control increment sequence, and applying the first control increment to the engine to output control instructions of fuel flow, mode switching factor and nozzle area. The application solves the problems that in the VCE thrust transition state or mode switching task, a fixed terminal target of BETA_MSV and A8 must be preset through performance optimization method, and direct fuel square punishment leads to the problem that fuel unconditionally tends to the lower limit.

The invention belongs to the technical field of data processing, and particularly relates to an online analysis instrument nonlinear error correction method based on manifold learning, and the method comprises the steps: obtaining a high-dimensional feature vector set containing original physical quantity readings and auxiliary environment parameters; and calculating a signal transient response entropy and an environmental coupling stress index of the data of each dimension, and further deducing a manifold tangent space distortion rate representing the bending degree of a data structure. On this basis, distortion weighted distance measurement is constructed to replace a traditional Euclidean distance, high-dimensional features are mapped to a low-dimensional space by using an improved local linear embedding algorithm, and finally an error prediction model is established through a least square support vector machine. According to the method, physical perception measurement is introduced, so that the problems of data manifold curling and Euclidean distance failure caused by sudden change of the environment are reduced, neighborhood selection errors are avoided, and the measurement precision and stability of the instrument in the multi-physics coupling environment are improved.

The invention relates to a method for monitoring transient thermal boundary conditions of a membrane water wall, which comprises the following specific steps of: establishing a local linear heat transfer model by drawing up discrete points of convective heat transfer coefficients, and calculating a sensitivity coefficient; setting initial values of heat flow radiated towards the fire side and fluid temperature in the pipe; establishing a temperature prediction model; rolling optimization is adopted to obtain a thermal boundary condition compensation quantity component; a weighting coefficient is constructed based on the unexposed side heat insulation boundary condition deviation; weighting the comprehensive compensation amount and correcting a guess value to obtain a radiation heat flow and fluid temperature monitoring result; and meanwhile, discrete points are weighted to output a convective heat transfer coefficient. According to the method, the monitoring rate is remarkably increased, the hysteresis effect is reduced, the dynamic response is enhanced, nonlinear changes are accurately processed, and the engineering practicability is high.

The invention relates to the technical field of industrial control systems, in particular to a self-adaptive control method for pet food production equipment, which comprises the following steps: acquiring key parameters such as raw material types, melt temperature, stacking density and specific mechanical energy; calculating a three-dimensional control feasible region boundary between the key quality attributes by using a process mapping model; inputting the feasible region boundary as a control constraint and the real-time key quality attribute into a multi-target optimization agent, executing a target planning algorithm, calculating an optimal control point by combining the feasible region boundary with the real-time key quality attribute, determining an optimal operation point agent corresponding to the generated optimal control point, determining the optimal control point, and performing target planning on the optimal control point. And inputting the optimal control point into three control loops, updating a process gain matrix through local linearization, and outputting a cooperative signal to adjust equipment through feedforward decoupling matrix compensation. According to the invention, through adaptive target determination and decoupling control, the product quality consistency is improved.

The invention relates to the technical field of cryptographic analysis, and discloses an automatic linear path analysis method and system for an ARX type cryptographic algorithm, and the method comprises the following steps: constructing a round function, configuring a linear analysis target, and selecting a search strategy; performing linear mask initialization on the round function, and establishing an association relationship between inter-round mask variables; generating local linear mask propagation expressions based on the linear approximation characteristic of each basic operation unit, combining the local linear mask propagation expressions into a global linear approximation model, and calculating a correlation expression of a linear path; executing a search strategy on the global linear approximation model; converting the global linear approximation model and the search strategy into constraint description and solving the constraint description; according to the method, the modeling complexity of linear analysis is reduced through visual modeling and selection of the path search strategy, and automation of the linear path search process is achieved.

A threshold calibration method for autonomous vehicle-pedestrian interaction at intersections based on the spatiotemporal evolution of driving safety fields is proposed. This method addresses the problems of delayed intent capture, low coupling with the traffic environment, and non-analytical threshold calibration in vehicle-pedestrian interaction at intersections. It constructs a posterior spatiotemporal backtracking mechanism to inversely compensate for perception-decision delays, accurately pinpointing the moment of interaction motivation and corresponding key field strengths. A multi-dimensional feature vector space coupling physics, topology, and traffic semantics is constructed, and a differentiable neural network is used to achieve the mapping learning from heterogeneous features to interaction decisions. Combined with path analysis, the nonlinear learning process is analyzed into locally linear equations, and the decision sensitivity partial derivative operator is extracted to establish an adaptive calibration equation to achieve dynamic evolution of the interaction threshold. This invention achieves prior recognition of interaction intent, enhances dynamic adaptability to complex scenarios, optimizes the continuity of the vehicle-pedestrian interaction model, and improves the interactive intelligence, driving smoothness, and safety redundancy of autonomous vehicles at intersections.

The invention relates to the technical field of data processing, in particular to a pumping unit bearing fault diagnosis method based on multi-structure local linear embedding, which comprises the following steps: acquiring to-be-diagnosed original data in a pumping unit, preprocessing the original data to obtain a pre-diagnosis data set, performing dimension reduction processing on the original data in the pre-diagnosis data set to obtain a pre-diagnosis data set; the method comprises the following steps: obtaining a least square structure and a sparse structure of original data in a low-dimensional space, fusing the obtained least square structure and sparse structure, constructing a low-dimensional reconstruction function, and processing the original data by using the low-dimensional reconstruction function to obtain a low-dimensional feature data set. According to the method, the oil pumping unit bearing feature extraction can be more accurately and efficiently realized, so that the obtained low-dimensional embedding result can better reflect the real operation state of the rolling bearing, and the problems in the operation of the oil pumping unit can be timely found and solved.

This invention provides a network security monitoring method and system based on distributed nodes. By acquiring node interaction records and protocol interaction identification information of each node in a distributed network within a preset communication period, a node communication dataset is obtained. A multi-dimensional phase space reconstruction matrix containing communication time-series correlation features and protocol identification correlation features is constructed. Nonlinear dynamic feature analysis is performed on this matrix to extract a set of chaotic feature values ​​of node communication behavior. A topological correlation structure of node communication features is constructed through local linear relationships. The set is mapped to a topological space of a preset dimension to generate an attack pattern topological expression vector. Finally, based on this, the propagation process of abnormal states in the distributed network is analyzed through inter-node energy propagation rules to obtain network security detection results. This invention can effectively improve the accuracy and dynamic analysis capabilities of distributed network security monitoring.

This invention discloses a collaborative encirclement method and system for unmanned surface vessels (USVs) using DMNMPC (Distributed Non-Controlled Control) combined with AC (Aggressive Containment) encirclement conditions. The method includes the following steps: establishing a self-organizing distributed control model and constructing a cost function and constraints for collaborative encirclement; based on the cost function and constraints, forming an initial encirclement that meets the AC encirclement conditions at a distance outside the detection range of the escape vessel; within one control cycle, obtaining the current nonlinear USV dynamic model and locally linearizing it to obtain a locally linearized model; optimizing the control input sequence of the control vessel according to the predicted states of the escape vessel and its neighboring teammates to obtain the optimal control input sequence; inputting the first control quantity of the optimal control input sequence into the control vessel to obtain the device for the next control cycle, until the escape vessel is successfully encircled.

The application provides a mobile phone extreme charging wideband variable current adaptive regulation method based on deep learning, and relates to the technical field of mobile phone extreme charging. The method comprises collecting multi-dimensional operation data of the mobile phone extreme charging scene. A local linear embedding algorithm is used for feature extraction. A state of charge evaluation model based on a deep belief network is constructed to evaluate the operation state of the mobile phone extreme charging system in real time. An echo state network is used to construct a dynamic correlation relationship model of multi-dimensional data. A multi-objective optimization model is established, and a multi-objective artificial bee colony algorithm is used to solve the multi-objective optimization model to select the optimal variable current regulation strategy of the current charging condition. The variable current parameters are adjusted. The application is constructed through deep fusion of multi-dimensional data and a deep learning model, and the variable current regulation strategy is optimized by combining an intelligent algorithm, so that the charging efficiency is improved, the battery temperature rise and circuit loss are reduced, the intelligence and adaptability of the variable current regulation are enhanced, and the battery safety is ensured and the system service life is prolonged.