174 results about "Adaptive identification" patented technology

The invention provides an urban drainage pipe network monitoring data cleaning and intelligent prediction method, and the method comprises the steps: firstly obtaining pipe network monitoring data, and carrying out the classification tracking and repairing of missing values; adopting a dynamic IQR algorithm based on a sliding window to adaptively identify abnormal candidate points; secondly, introducing a pipe network topological relation, comparing upstream and downstream data change trends, eliminating non-physical anomalies caused by equipment faults, and reserving real hydraulic events; calculating the physical delay time between the nodes by using the cross correlation coefficient; and finally, constructing a random forest model, taking upstream historical data after delay alignment as feature input, and realizing accurate prediction of a future water level and quantification of a feature contribution degree. According to the method, a physical mechanism and machine learning are fused, the problems that data cleaning lacks adaptivity and a deep learning model lacks interpretability are effectively solved, and the accuracy of waterlogging early warning is improved.

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing image adaptive identification method and system for territorial space planning, and the method comprises the steps: obtaining a multi-source remote sensing image data set of a research region, feature extraction, cloud detection, quality evaluation and adaptive preprocessing are carried out; carrying out prototype network coding, calculating a category prototype and probability, and supporting fine tuning of a set; carrying out multi-scale cavity convolution and category scale attention fusion; evaluating the adaptability score of the comprehensive fusion feature map set, and carrying out weighted fusion, classification and normalization; change detection is carried out, stable and change regions are segmented, and time sequence context features are extracted and constrained optimization is carried out; entropy is fused, a boundary is decided, uncertainty is estimated, and weighted fusion is carried out according to a change area; conditional random field optimization, confidence level grading and connected domain identification are carried out; the automation level, the adaptive capacity and the recognition reliability of remote sensing monitoring of territorial space planning are improved.

The invention belongs to the technical field of hypersonic flight vehicle control, and relates to a rigid-elastic coupling-oriented active and passive integrated control method for a hypersonic flight vehicle. The invention aims to realize stable tracking control of the elastic hypersonic flight vehicle. The method comprises the following steps: constructing a longitudinal dynamic model of the elastic hypersonic aircraft; self-adaptive identification of the elastic vibration frequency is realized through a cascaded self-adaptive filter; an elastic modal filtering estimation method is designed, and high-precision and low-cost elastic modal state quantity is provided for subsequent active feedback controller design; and then rigid-elastic coupling model decomposition is carried out, the control performance is ensured by using active disturbance rejection passive control for a rigid body subsystem, an RBF neural network is introduced for an elastic subsystem, an elastic mode is actively inhibited by using sliding mode control, and stable tracking of a reference instruction is realized. The method is an active and passive integrated control method for the hypersonic flight vehicle oriented to rigid-elastic coupling, and the application prospect is wide.

The invention discloses a land space use control compliance intelligent identification and auxiliary determination system, and relates to the technical field of digital treatment. A data management module receives multi-source heterogeneous time sequence data to construct a dynamic graph structure substrate, and a probability scheduling module predicts global potential conflict probability by using a space-time graph neural network and performs calibration in combination with a historical conflict database; the graph enhancement module positions a key conflict path and a core node by reversely updating a conflict knowledge graph, and the reasoning execution module activates a rule engine to execute accurate matching in a region to be subjected to fine detection and outputs a deterministic conflict identifier; the fusion decision module finally fuses probabilistic prediction and rule judgment results to generate a mixed conflict list, and the feedback module sends the mixed conflict list to the execution terminal and optimizes the model based on feedback. According to the method, multi-level and self-adaptive identification of the land and space purpose conflict from wide-area prediction to accurate judgment is realized, and the supervision efficiency and the decision accuracy are improved.

The invention relates to the technical field of charging optimization, and discloses a multi-stage charging optimization method based on adaptive identification of a battery, and the method comprises the following steps: S101, in-situ detection and battery type identification; step S102, applying a current step and collecting a voltage response; s103, predicting the change of the terminal voltage along with time; step S104, determining a safe amplitude limiting current; step S105, determining a pre-deceleration factor; and step S106, judging full power, generating a quota recovery instruction, and returning to the step S104 for redistribution. Battery individual parameters are obtained through in-situ detection and micro-pulse identification, current quotas are dynamically distributed based on a constant voltage proportion, a safe target current is determined by combining voltage and power amplitude limiting, current distribution unbalance is avoided, the bus current utilization rate is maximized, voltage overshoot is inhibited by means of a first-order following strategy and a pre-deceleration factor, and the safety of a battery is improved. And stable switching is ensured by matching with constant-voltage inlet current clamping.

The invention discloses a multi-element mixed enhanced intelligence-based cross-material pavement disease adaptive identification method, relates to the technical field of road intelligent operation and maintenance, and solves the technical problems that the identification rate is suddenly reduced and the false detection and omission ratio is high in a cross-material scene due to the fact that a model is developed for a single material or a general model is used in the prior art. The method comprises the following steps: S1, constructing a cross-material pavement disease image sample library; s2, constructing a pavement disease identification visual large model; s3, constructing a pavement disease pixel-level integrated enhanced intelligent recognition model; and S4, inputting a to-be-detected image to the trained pavement disease identification visual large model and the pavement disease pixel-level integrated enhanced intelligent identification model respectively, obtaining fusion output according to the adjusted fusion weight, and finally realizing pavement disease multivariate hybrid enhanced intelligent identification. The method can be simultaneously suitable for disease identification tasks in various material environments such as asphalt pavements, cement pavements and composite material pavements, and high-precision intelligent identification in a cross-material scene is realized.

The invention discloses an edge AI-based battery thermal runaway prediction method during transportation of an electric vehicle, and the method comprises the steps: collecting multi-dimensional parameters of a battery pack during transportation of the electric vehicle, and carrying out the data fusion; performing adaptive identification based on the fused data to obtain a current scene type; then, endowing scene adjustment weights to the temperature gradient analysis data; performing fusion processing on the scene adjustment weight and the voltage fluctuation monitoring data, and extracting internal resistance change detection data; when the internal resistance change detection data exceeds a preset threshold value, generating an abnormal indication signal, extracting a dynamic change mode of multi-dimensional parameter fusion, updating a weight parameter, and obtaining a real-time weight vector; performing matrix operation on the fused data according to the real-time weight vector to obtain a comprehensive risk feature vector; and inputting the comprehensive risk feature vector into a machine learning model, determining a real-time risk assessment level, and obtaining a thermal runaway prediction result. The prediction precision and real-time performance of the thermal runaway risk of the battery pack in a complex environment are remarkably improved.

The invention relates to the technical field of video acquisition, and discloses an unmanned aerial vehicle video acquisition system and method combined with artificial intelligence, and the system comprises a scene distribution modeling module, a causal decoupling module, a sparse reasoning module, an evidence fusion module and a parameter adjustment module. The system achieves the comprehensive effects of scene adaptive identification and resource dynamic allocation, feature generalization learning and detection robustness enhancement, calculation load intelligent regulation and control and abnormal information isolation, multi-scale evidence reliable fusion and parameter autonomous optimization. Aiming at the problem of unreasonable resource allocation caused by the fact that a fixed detection strategy in the background technology cannot adapt to scene change, the system adopts real-time statistical modeling and scene type recognition technologies to realize matching of detection resources and scene requirements, and a traditional detection model excessively depends on training data distribution to cause insufficient generalization ability.

The invention relates to the technical field of CIP cleaning, and discloses an adaptive pulse flow CIP cleaning control and energy efficiency optimization system, which comprises a data acquisition module, an intelligent decision and decision generation module, an execution and control module, a feedback and optimization module and a maintenance and management module, through a multi-mode flow field intelligent control and cleaning object self-adaption identification technology, various scenes of CIP cleaning can be comprehensively covered, conventional cleaning efficiency indexes are concerned, elements such as pipe diameter adaptability, dirt type pertinence and flow field mode adaptability are also included, the cooperation process of pulse parameters and a flow field is deeply optimized, and the cleaning efficiency is improved. The cleaning effect can be more accurately matched with real requirements of different cleaning objects, and a more scientific basis is provided for high efficiency and pertinence of industrial cleaning.

Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

The invention relates to the technical field of information security, and discloses a method for analyzing risk information in public data based on multi-modal artificial intelligence, and the method comprises the steps: collecting multi-modal data, and carrying out the data preprocessing of the multi-modal data; extracting high-dimensional features of the preprocessed modal data; obtaining a confidence coefficient score of the high-dimensional feature of each modal data; inputting the modal availability vector and the confidence score into a multi-modal fusion model, and calculating an attention fusion weight; performing weighted fusion on the high-dimensional features of the modal data according to the attention fusion weight to generate multi-modal joint feature representation; based on the multi-modal joint feature representation, outputting a risk probability and a type label; and when the output risk probability exceeds a preset threshold value, generating a structured early warning event, and automatically pushing the structured early warning event to a subsequent processing system to trigger a linkage response. According to the method, the risk information in the public data is accurately, efficiently and adaptively identified and disposed, and the automation level and response efficiency of network security protection are effectively improved.

The invention provides an energy-saving optimization method and device for a thermal power auxiliary engine system, electronic equipment and a storage medium, relates to the technical field of data processing, and aims to realize multi-physical-scale and multi-time-scale state estimation of the thermal power auxiliary engine system by constructing a hybrid dynamic model fusing a mechanism model and a time sequence prediction model. Meanwhile, on-line self-adaptive identification and correction are carried out on key operation parameters in the hybrid dynamic model based on the state estimation result so as to track the performance change of the auxiliary machine equipment, and a collaborative optimization control strategy of the system is generated by using the corrected hybrid dynamic model and adopting a multi-target dynamic optimization algorithm under the condition that a preset operation constraint condition is met. And the collaborative optimization control strategy is converted into an executable control instruction through a hierarchical coordination control mechanism, and the executable control instruction is issued to a field controller of each auxiliary machine equipment, so that the problem of relatively large deviation of an optimization result effect caused by limited processing capacity of a thermal power auxiliary machine system under a dynamic working condition and difficulty in self-adaptive updating of model parameters can be solved.

Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

The invention relates to the technical field of shale identification, in particular to a shale gas sweet spot self-adaptive identification and optimization prediction method based on machine learning, which comprises the following steps of: identifying a reservoir physical property mutation interface in a well depth direction, and performing discontinuous layer section division based on a brittleness evolution trend, the discontinuous interval division is used for generating an unsteady reservoir unit set; a machine learning model with stage perception ability is constructed by introducing a jump type productivity criterion, a dessert score body with hierarchical constraint is output, and the dessert score body represents a productivity potential level and an activation probability of each reservoir unit; and correcting the dessert score body by using model residual feedback, and outputting a final dessert prediction map with unsteady features and interlayer differential expression ability. According to the method, the attention mechanism is introduced to enhance the recognition of the key productivity transition stage, so that the accurate prediction of the productivity response of the reservoir unit is realized.

The invention discloses a self-adaptive identification and compensation method for friction nonlinear characteristics of a feeding system of a numerical control machine tool. According to the method, parameters of a LuGre friction model are initialized by collecting an actual position signal, an actual speed signal, an actual current signal and an instruction position signal of a feeding system; friction parameter self-adaptive identification is carried out based on motion state segments, coulomb friction parameters and viscous friction parameters are updated by adopting a least square method in a constant speed stage, and bristle rigidity parameters, bristle damping parameters and Stribeck speed parameters are updated by adopting an extended Kalman filter in an acceleration stage and a deceleration stage; generating a friction force estimation value according to the identified friction parameter, converting the friction force estimation value into a compensation current signal, and adding the compensation current signal to a servo driver current instruction after phase lead compensation. According to the method, the friction characteristic change can be adaptively identified on line, tracking errors and vibration caused by friction nonlinearity are effectively inhibited, and the machining precision and stability of the numerical control machine tool are improved.

The invention discloses a retired battery parameter identification method, and relates to the technical field of lithium ion battery management, and the method comprises the steps: carrying out the pulse charging and discharging test of a retired lithium ion battery, obtaining a relaxation voltage sequence, carrying out the smoothing preprocessing of the relaxation voltage sequence, constructing a candidate cut-off termination time set in a relaxation time interval, and carrying out the calculation of the candidate cut-off termination time set. And intercepting corresponding relaxation data for each truncation termination time, performing parameter fitting based on the second-order equivalent circuit model, calculating a root-mean-square error at each truncation termination time, and selecting a parameter group with the minimum error as a target equivalent circuit parameter. By selecting and modeling a data fitting interval as a root-mean-square error minimization problem, automatic adaptive identification of the equivalent circuit parameters of the decommissioned battery is realized, the difficulty of selecting an E point under a fixed window is solved, the robustness and physical consistency of a parameter identification result are improved, a model prediction error is remarkably reduced, and the generalization ability under multiple working conditions is improved.

The application relates to the field of artificial network control, in particular to an artificial network control method, device and system for shielding across a darkroom. By using a fiber link and an internal battery power supply architecture in a fully-closed shielding darkroom, the interference of personnel on a test environment is completely eliminated, monitoring of parameters such as internal temperature and voltage of the integrated darkroom is added, zero-crossing synchronization noiseless operation of phase switching is realized, in addition, user end environment adaptive identification, driving parameter dynamic calibration and equipment life prediction are realized, and the needs of high-precision, unattended automatic phase switching and testing are met.

The application provides a cross-day-and-night vehicle re-identification method, device and equipment with a glare suppression function. The method comprises the following steps: obtaining a to-be-identified vehicle image, inputting the to-be-identified vehicle image into a vehicle re-identification model, and obtaining a vehicle matching result. The vehicle re-identification model is obtained by training a pre-constructed re-identification model based on sample images. The sample images include daytime collected vehicle sample images or nighttime collected vehicle sample images. The pre-constructed re-identification model comprises a day-and-night glare extraction module, a glare suppression module, a first residual module and a cross-domain feature fusion module. The application realizes an adaptive identification suppression mechanism for a glare area in the to-be-identified vehicle image. Moreover, the setting of the cross-domain feature fusion module realizes feature fusion and feature alignment for the to-be-identified vehicle image across time periods, which is beneficial to reducing domain differences and improving the generalization ability and robustness of the vehicle re-identification model.

The invention discloses a wearing position identification method, a storage medium and an earphone. The method comprises the following steps: detecting whether a user takes the earphone in an earphone box or not; if yes, triggering an acceleration sensor in the earphone to collect an initial posture parameter from the time when the user takes the earphone to the time when the earphone enters the ear; determining an attitude mode based on the initial attitude parameters within a preset specific acquisition duration; acquiring a target axis corresponding to the attitude mode; determining motion trend information of the earphone based on the initial attitude parameter and the target axis; and recognizing wearing position information of the earphone according to the motion trend information. Compared with the prior art, the method and the device have the advantages that the initial posture parameters are acquired through the acceleration sensor to determine the corresponding posture mode and the target axis, so that adaptive identification of the wearing position of the earphone is realized, the accuracy and the efficiency of earphone wearing position identification under different earphone wearing postures are improved, and the use experience of a user is improved.

The invention belongs to the technical field of micro-grid protection, and particularly relates to a micro-grid integrated protection method based on multi-criterion self-adaptive recognition, which comprises the following steps: in a micro-grid, constructing a three-layer control architecture of a perception layer, a decision layer and an execution layer; the sensing layer is used for collecting electrical quantity data of the micro-grid in real time, and the current operation mode of the micro-grid is recognized in real time on the basis of the power flow direction and frequency deviation of a grid-connected point in the decision-making layer; in a decision-making layer, a multi-dimensional feature fusion algorithm is adopted to fuse fault feature parameters in a grid-connected mode or an island mode, and a fault state is judged; the decision-making layer identifies the current operation mode of the micro-grid, generates an instruction and issues the instruction to the execution layer, and the execution layer executes mode switching; and when the fault is judged to occur, the fault position is positioned and isolated, reclosing recovery is tried for a transient fault, and power supply of a non-fault area is guaranteed by a permanent fault adjustment operation strategy. According to the invention, accurate fault identification and stable control in the grid-connected / island mode switching process can be realized.

The application relates to the technical field of mechanical arms and discloses a mechanical arm kinematic parameter self-adaptive identification system based on binocular vision guidance, which comprises the following modules: a modeling module obtains joint angles and temperatures to construct an augmented parameter set, calculates an end theoretical pose, a joint Jacobian matrix and a task Jacobian matrix; a vision module obtains measurement parameters through a binocular camera and calculates a visual confidence matrix; a monitoring module constructs a Hessian matrix based on the Jacobian matrix and the confidence matrix and sends a micro-activation instruction when a condition number exceeds a standard; a control module generates joint micro-offsets in combination with a tolerance band and triggers the binocular camera to collect actual measurement poses according to torque derivatives; and an updating module updates the augmented parameter set by using the residual errors of the actual measurement poses and the theoretical poses in combination with the confidence matrix. The application utilizes binocular vision guidance and stable torque triggering, overcomes the coupling of structural thermal deformation and error parameters, realizes high-precision mechanical arm kinematic parameter self-adaptive identification, and improves calculation stability.

The application relates to the field of artificial intelligence and discloses a target recognition method and system based on deep learning, which comprises the following steps: acquiring visible light and infrared images of the same scene, generating illumination invariance features through an adversarial training network; modeling the feature sequence by using a space-time joint network to generate an initial recognition result; performing space-time verification on the initial recognition results of continuous multiple frames by using a Markov random field model to generate a calibrated recognition result; and performing online model calibration on the space-time joint network based on the correction deviation between the initial recognition result and the calibrated recognition result. The application also provides a recognition system for executing the method. Through the synergistic mechanism of multi-modal feature decoupling, space-time joint modeling and verification, and online feedback calibration, the application effectively improves the all-weather adaptability, recognition accuracy and trajectory continuity of the system in complex scenes such as illumination changes and target occlusions.

The invention provides a high-efficiency magnetic field optimization method for a wind power electromagnetic brake, and belongs to the technical field of wind power electromagnetic brakes. Air-gap magnetic field distribution is calculated by using a fast multi-pole expansion algorithm, and feedforward compensation is performed on coil excitation current through a Preisach inverse model adaptive identification algorithm to improve the uniformity of a magnetic field; an operator splitting method is adopted to solve a magnetic-thermal coupling problem, material parameters are corrected according to temperature, an optimization objective function including air gap magnetic flux density maximization, magnetic field uniformity minimization and temperature constraint is established, Latin hypercube sampling is utilized to generate a training data set, optimization iteration is carried out through a Kriging agent model in combination with an expectation improvement criterion, and a training result is obtained. And outputting the optimal design parameters. The technical problems that the calculation efficiency of the wind power electromagnetic brake in the magnetic field optimization process is low, and the hysteresis nonlinear characteristic and the multi-physical field coupling effect are difficult to accurately consider are solved.

The application discloses an abnormal station identification method and system based on observation values and distance dynamic threshold values, and relates to the technical field of observation station data quality control. The method comprises the following steps: acquiring verified qualified long sequence of hourly precipitation data of observation stations and observation station position data; constructing a relationship function of the distance of adjacent observation stations and the difference threshold value of hourly precipitation, and a relationship function of the hourly precipitation of observation stations and the difference threshold value of hourly precipitation of adjacent observation stations; on the basis, calculating the data anomaly probability of all observation stations in the analysis period; and judging the observation station grade according to the data anomaly probability. The application breaks through the limitation of traditional fixed threshold value by fusing the observation value size and the station distance to dynamically construct a double threshold function, can realize accurate and adaptive identification of abnormal data of observation stations, and improves the reliability and business application value of ground observation data.

The invention discloses a method for identifying left and right earphones, a storage medium and earphones. The method comprises the following steps: detecting whether a user takes an earphone in an earphone box in an open box state or not; if yes, triggering an acceleration sensor in the earphone to collect initial posture parameters from the time when the user takes the earphone to the time when the user wears the earphone; preprocessing the initial attitude parameter to obtain a specific attitude parameter; based on the specific posture parameters, obtaining motion trend information of the earphone; and recognizing wearing position information of the earphone according to the motion trend information. Compared with the prior art, the motion trend information of the earphone is obtained through the initial posture parameters of the earphone collected by the acceleration sensor in the earphone, and then the wearing position information of the earphone is recognized, so that the use of additional hardware is reduced, the occupation of a wearing position recognition function on chip computing power is reduced, the power consumption of the earphone is reduced, and the user experience is improved. The self-adaptive identification without distinguishing wearing of the left earphone and the right earphone is realized, and the use experience of a user is improved.

The invention discloses an AI-based multi-language adaptive recognition method, and relates to the technical field of language information processing, and the method comprises the following steps: collecting rhythm features and pause nodes in a continuous voice stream, extracting a speed change track, and generating a rhythm basic draft for representing the rhythm change trend of a voice signal in a time dimension; and performing speed change decomposition on the voice signal based on the rhythm basic draft, determining a speed increasing area and a speed slowing area, generating a time adjustment table, and recording the duration and rhythm span of each speed change section. According to the invention, through double-layer time control of a rhythm basic draft and a time adjustment table, dynamic time mapping and feature extraction continuity under speech speed change are realized; and through a dynamic rhythm adjusting mechanism, executing time extension in a speech speed increasing area, executing rhythm forward movement in a speech speed reducing area, and correcting semantic dislocation and fracture in real time, so that speech recognition keeps semantic integrity and recognition stability in a multi-language and variable-speed scene.

The present application relates to radar target recognition tracking technical field, especially in the field of phased array radar multi-target adaptive identification method and system, including: first, the radar echo and platform attitude data are acquired, the velocity distribution is extracted and the target is divided into two groups of high speed / low speed; for high speed group, the displacement prediction parameter is extracted to adaptively adjust the beam pointing sequence, for low speed group, the pulse repetition frequency is determined based on the residence time window and the frequency shift characteristics, and the resource allocation scheme is generated; then the attitude correction result is integrated into the beam control parameter, the adaptive observation sequence is generated combined with the resource scheme, and the trajectory information is tracked in real time; again, the Doppler shift deviation is estimated according to the trajectory, the gain / bandwidth and other signal processing parameters are compensated online, and the corrected target position and velocity are output. The present application realizes three-dimensional high-precision identification, continuous stable tracking and efficient resource utilization in complex dynamic scene.

The invention discloses a multi-mode signal real-time self-adaptive recognition and extraction method for a complex scene, solves the problem that an existing interference suppression and signal extraction algorithm cannot adapt to gravitational wave measurement requirements, belongs to the technical field of digital signal processing, and adopts a fixed sequence of main carrier clock sideband pseudo code signals to perform signal extraction and separation. According to the sequence, the processing efficiency is optimized, the weak signal is prevented from being covered by the strong signal, and the interference of subsequent processing is remarkably reduced. Compared with parallel or disordered processing in the background technology, the method provided by the invention has the advantages that strong interference components are stripped in sequence, so that rapid convergence in real-time operation is ensured, and the robustness and precision of overall signal extraction are improved.

The invention discloses a self-adaptive recognition system for medical capsule endoscope streaming images, and the system comprises a feature extraction module which is used for receiving a real-time image stream obtained in a scanning process, and extracting the features of a current frame image; the feature buffer comprises a feature slot position and an activeness counter, the feature slot position is used for storing stock features of the scanned intestinal tract area image, and the activeness counter is used for recording the activeness of the feature slot position; the updating module is used for updating the activeness of the feature slot position; the gain adjusting module is used for generating a gain weight aiming at the current frame image through bidirectional biological plasticity adjusting logic; the training optimization module is used for injecting the gain weight into a loss calculation loop of the classifier module and correcting physical parameters of a classifier in real time through back propagation; and the classifier module is used for outputting a prediction classification result according to the current frame image features. According to the method, the problem of model performance degradation caused by non-uniform motion states of the capsule endoscope can be relieved, and the prediction accuracy is improved.

The application provides a new energy control parameter self-adaptive identification method based on double-layer particle swarm optimization, and belongs to the technical field of power system simulation analysis. Typical control strategies of heterogeneous new energy units are comprehensively used to construct a strategy database and perform verification; test data are acquired for processing and feature extraction, so that high and low threshold values, current initial states and test feature data are obtained; a fitness function is constructed based on the strategy database, a discrete particle swarm algorithm is used in the global optimization layer to select an optimal strategy combination from the strategy database, and in the local optimization layer, parameters of the selected strategy are continuously optimized in combination with a momentum factor and a learning rate. The application uses actual unit test data characteristic values as inputs of the double-layer particle swarm algorithm, determines an optimal strategy-parameter pair through a total error index weighting, verifies the accuracy of the strategy-parameter pair, and effectively improves the efficiency and precision of modeling of heterogeneous new energy equipment.