178 results about "Parametric identification" patented technology

The invention discloses a driver controller detection system, which comprises an acquisition module, an acquisition module, a processing module, an identification module, a judgment module, an analysis module and a generation module, the acquisition module captures contact information and electromagnetic interference data of contacts in real time by means of a high-dynamic voltage sensor and a broadband EMC probe; the acquisition module is triggered by a locomotive signal and records dynamic working condition data; the processing module aligns the data, performs denoising and extracts fault parameters; the identification module scans the voltage waveform and judges the transient contact failure; the judgment module monitors EMC data and compares an instruction position, and judges misoperation; the analysis module analyzes fault association, judges a composite type and triggers an alarm; the generation module generates a detection report and a log. According to the system, a multi-dimensional monitoring network is constructed under a dynamic working condition, data reliability is guaranteed, faults are accurately identified, a compound fault source is positioned, and the safety and stability of locomotive operation are improved through risk grading early warning and automatic reporting.

The invention provides a method and device for detecting vehicle falling into water, a vehicle and a storage medium, the method is applied to the technical field of vehicle falling into water detection, the method comprises the steps that M falling into water detection parameters are obtained through M collection devices of different types in the vehicle, the falling into water detection parameters are used for representing the falling into water state of the vehicle, and M is a positive integer larger than 1; determining M basic probability distribution values corresponding to the M collection devices according to the M water falling detection parameters; according to the M basic probability distribution values, the falling-into-water confidence coefficient of the vehicle is determined; and when the falling-into-water confidence is greater than or equal to the preset confidence, determining that the vehicle is in a falling-into-water state. According to the method, whether the vehicle falls into water or not can be identified by fusing the falling-into-water detection parameters collected by multiple sensors, the reliability and robustness of vehicle falling-into-water detection can be effectively improved, and misjudgment caused by identification of single equipment is avoided.

The invention belongs to the field of power distribution network parameter identification, and discloses a power distribution network line parameter identification method and system based on a graph neural network, and the method comprises the steps: employing a unified projection and attention weighting mechanism, obtaining the point features of a power distribution network line, and carrying out the self-adaption integration of multi-source heterogeneous observation data; initial edge features are explicitly constructed in neural network propagation of each layer of graph, node and edge representation is alternately updated, and the line parameter prediction precision is improved; power grid core laws such as Ohm's law and node power injection constraint are converted into a loss function to constrain a training process, consistency constraint of a parameter prediction result and a power system physical law is realized, and interpretability and credibility of the prediction result are improved; and inputting the power grid data into the graph neural network optimization model, and outputting a power distribution network line parameter identification result, thereby solving the problems that in the prior art, dynamic information interaction between nodes and edges is neglected, so that parameter identification is sensitive to network structure change, and physically incredible parameter output is easy to generate.

The invention discloses a recommendation system-oriented privacy sensitive parameter identification and accurate deletion method, and relates to the technical field of recommendation system model optimization and privacy protection. In order to solve the problems that privacy sensitive parameters in an existing recommendation system model are difficult to accurately position and the model performance loss is too large after deletion, according to the evaluation thought of parameter importance and data feature relevance, importance scores of model parameters on two types of data sets are calculated respectively by dividing a forgetting set and a reservation set, and the privacy sensitive parameters are obtained. The privacy sensitive parameters with the forgetting set importance higher than the reserved set importance are screened out, and mask zero setting processing is carried out. According to the method, the model does not need to be retrained or subjected to weight updating, sensitive parameter recognition and deletion can be completed only through single-time forward propagation, and the original recommendation performance of the model is reserved to the maximum extent while the privacy security of a recommendation system is guaranteed. According to the method, accurate identification of privacy sensitive parameters is realized on a mainstream recommendation model, the reduction range of model recommendation accuracy after deletion is controlled within an acceptable range, and the privacy leakage risk is remarkably reduced.

The invention discloses a DC-DC converter parameter identification method based on digital twinning, and the method comprises the following steps: firstly building a Buck converter simulation model, collecting needed data from the simulation model, building a mathematical model according to a Buck converter circuit structure, KCL and KVL, and carrying out the calculation of the Buck converter simulation model. And solving a differential equation of the mathematical model by using a Runge-Kutta method to obtain calculation data, substituting the collected simulation data and calculation data into a target function, performing iterative optimization on the target function value by using a Kirchhoff's law optimization algorithm, and outputting an optimal solution of a to-be-identified parameter, namely an identification value of the circuit parameter. According to the method, the parameter identification error of the DC-DC converter can be reduced, and the accuracy is improved.

The invention discloses an aircraft wake flow identification method, system and device based on a wind lidar and a storage medium, and belongs to the technical field of radar detection. The method comprises the following steps: acquiring low-resolution laser radar detection data; calculating the central position of an aircraft trailing vortex pair according to the low-resolution laser radar detection data, and dividing the low-resolution laser radar detection data based on the central position of the aircraft trailing vortex pair to obtain an input region; and inputting the input region into a pre-trained aircraft wake flow super-resolution model to obtain a high-resolution reconstructed aircraft wake flow field. According to the invention, the problems of fuzzy wake flow structure, large vortex core position deviation and underestimation of vortex circulation caused by insufficient range resolution of a wind measurement laser radar in the prior art are solved, and the detection precision and parameter identification capability of the laser radar on aircraft wake flow are effectively improved.

The invention belongs to the technical field of unmanned aerial vehicle aircraft design, and particularly relates to a longitudinal aerodynamic parameter identification calculation method in a closed-loop state and a large angle-of-attack range, and the method comprises the steps: building a flight simulation model; performing aerodynamic characteristic simulation based on a flight simulation model to obtain an aerodynamic load value, comparing the aerodynamic load value with data tested in an actual flight experiment, and calculating and minimizing an error sum of squares; dividing the whole angle-of-attack range into a plurality of sub-intervals, respectively obtaining the optimal aerodynamic parameters of each sub-interval based on the minimum sum of squares of errors, inputting each optimal aerodynamic parameter into the flight simulation model, and carrying out model accuracy verification; and drawing an aerodynamic characteristic curve according to each optimal aerodynamic parameter, and verifying the applicability of the flight simulation model. The method has universality and is suitable for longitudinal aerodynamic coefficient parameter identification of different types of unmanned aerial vehicles, the parameter identification efficiency is improved, and meanwhile the applicability of the model under the complex flight working condition is enhanced.

The invention belongs to the technical field of material processing and manufacturing, and particularly relates to a multi-process complex loading process material deformation and performance unified prediction method and application thereof. Aiming at the technical defects of an existing material constitutive model in the aspects of cross-process applicability, multi-process historical effect characterization and formation-structure-performance whole-process unified evaluation, a unified internal variable system is constructed; by combining a multi-process unified constitutive theory, a multi-process composite loading path calibration method and a multi-data source collaborative parameter identification and simulation integration technology, unified prediction of deformation behaviors, structure evolution and post-forming performance of materials under different forming processes, different loading modes and complex historical paths is realized. Compared with the prior art, the method of the invention makes significant technical progress in the aspects of model versatility, prediction precision and engineering implementation convenience.

The invention relates to the technical field of motor rotor dynamics modeling, in particular to a motor rotor assembly modal modeling and parameter identification method and device and a medium. The method comprises the following steps: firstly, acquiring rotor assembly data, performing simplification and mesh generation, and constructing an equivalent transition layer model to accurately simulate connection boundary characteristics by combining finite element analysis with component connection relation definition; material parameters are calculated based on the model, the inherent frequency and the vibration mode are extracted, and actual modal parameters are identified through modal analysis and experimental verification; performing data arrangement and parameter extraction on an identification result, and obtaining an optimization model by adopting modular processing and optimization calculation; and finally, performing experimental verification and error analysis on the optimization model to generate a final report. By introducing the equivalent transition layer and modular processing, the modal simulation precision and optimization efficiency of the rotor assembly are remarkably improved, and a reliable basis is provided for dynamic performance design and optimization of a rotor system.

The invention discloses an automatic detection method for dynamic balance parameters of an automobile hub, and the method comprises the steps: employing a piezoelectric ceramic array self-sensing main shaft technology, enabling detection reference establishment and signal collection to be integrated in a main shaft body, and carrying out the fusion of a hub installation process and a detection reference establishment process; synchronous acquisition of multi-dimensional vibration signals is carried out; an original mixed signal is decomposed into modal components in a self-adaptive mode, and environmental vibration interference is eliminated; a FastICA-RLS hybrid algorithm is adopted to separate an independent source signal from the preliminarily purified signal, and non-stationary interference is tracked and compensated in real time; constructing a flexible rotor model based on modal parameter identification, and calculating an unbalance amount; self-adaptive Kalman filtering error compensation and prediction are carried out, and the amount of unbalance after compensation is output; and an optimal counterweight scheme is automatically calculated based on the compensated unbalance amount, and visual display and guidance are performed through a human-computer interface, so that the detection precision and the detection efficiency are improved.

The invention discloses a method for identifying logging interpretation electrical standard parameters, and solves the problems of inaccurate difference and low efficiency caused by the influence of subjective factors on the manual identification result of the logging interpretation electrical standard parameters in the prior art. The method comprises the following steps: S1, establishing a benchmarking array for a well section to be identified; s2, matching the target well data on the apparent resistivity curve A based on the established benchmarking array; obtaining optimal matching data; s3, determining a reference layer identified by the electrical standard; s4, identifying a microsphere standard, a sound wave calcium judgment standard and a deep three lateral standard; s5, identifying a microelectrode standard; and S6, identifying a micro-gradient calcium judgment standard. The device for realizing the automatic identification of the logging interpretation electrical standard parameters comprises an establishing unit; a matching unit; a determination unit; a first identification unit; a second identification unit; and a third identification unit. According to the method, logging interpretation electrical standard parameters can be rapidly identified, and quality improvement and efficiency improvement of logging interpretation reservoir thickness division work are assisted.

The invention relates to the technical field of mechanical equipment monitoring, in particular to a rotary machinery gear transmission system kinetic parameter identification method, which comprises the following steps: step 1, based on a gear dynamics theory, considering meshing rigidity and meshing damping of a time-varying gear pair, and establishing a transmission chain torsional vibration kinetic model of rotary machinery; step 2, based on the transmission chain dynamic model established in the step 1, respectively establishing parameter identification objective functions of rotational inertia and meshing rigidity; 3, data of angular displacement, angular acceleration and torque of the transmission chain are continuously collected, and data preprocessing is completed; 4, reconstructing a target function of rotational inertia under the reference of the rotor coordinates, and solving the rotational inertia; step 5, based on the rotational inertia identified in the step 4, solving a meshing stiffness objective function; according to the method, the problem that the stability and the reliability of the gear transmission system cannot be accurately predicted by an existing identification method is solved.

This invention discloses a method for identifying vehicle modal parameters based on impact loads, comprising the following steps: S100: designing drop conditions according to the vehicle system's modal characteristics; S200: conducting drop tests on the test vehicle under the designed drop conditions; S300: collecting the vehicle's vibration response during the drop test and calculating the modal energy using the vibration response; S400: identifying modal parameters using the state variable method. This invention utilizes impact loads to excite the modal vibration response of the vehicle system and combines the modal energy method and the state variable method to accurately identify the modal parameters. This invention is simpler, lower cost, and more accurate, effectively identifying the modal parameters of a rail vehicle system without the need for complex simulations and high testing costs.

The invention provides a shale in-situ conversion physical property full-period evolution identification characterization method, and belongs to the technical field of shale in-situ conversion, the method comprises the following steps: selecting a shale sample, determining basic parameters, heating according to a preset heating rate under preset confining pressure, pore pressure and fluid boundary conditions, and carrying out online monitoring and staged sampling to obtain dynamic evolution parameters; in the identification evolution stage, candidate boundary confidence is calculated according to an evidence fusion theory and then combined with a preset stage semantic rule, conflict resolution is carried out by adopting a hierarchical priority strategy to generate a stage division result, uncertainty quantification is carried out by adopting an active learning strategy in combination with Gaussian process regression, and experimental temperature points are encrypted; and training a physical information constraint artificial intelligence prediction model and outputting a physical property dynamic evolution curve. The technical problem that the boundary of the physical property evolution stage in the shale in-situ conversion process is difficult to accurately identify is solved.

The application discloses a driving behavior risk assessment method, device, equipment and computer program product, comprising: acquiring vehicle driving parameters, vehicle safety system states, road images and external environmental factors of a target vehicle in a first historical period; identifying multiple target moments of high-risk driving operations of the target vehicle according to the vehicle driving parameters, inputting the vehicle safety system states, road images and external environmental factors corresponding to the target moments into a pre-trained driving scene recognition model to obtain target driving scenes; determining high-risk driving behavior events of the target moments according to the target driving scenes and operation types of the corresponding high-risk driving operations; and assessing a driving behavior risk level of the target vehicle according to the distribution of the high-risk driving behavior events in the first historical period, and generating driving behavior improvement suggestions. The application can help drivers improve driving habits, reduce accident risks and improve driving safety, and can be applied to the technical field of vehicle monitoring.

The invention discloses a multiple-input-multiple-output bridge modal parameter identification method and device based on a corrected periodogram method power spectrum, and the method comprises the steps: obtaining a section of multi-point input excitation and multi-point output response, and equally dividing the excitation and response data according to the dimension of an excitation vector; the equant excitation and response data are substituted into a response power spectrum matrix calculation formula for separating excitation, singular value decomposition is carried out on the obtained matrix to obtain a maximum singular value curve and a singular vector, the frequency corresponding to the peak value of the curve is the inherent frequency of the bridge, the corresponding singular vector is the vibration mode of the bridge, and the maximum singular value curve is obtained. The frequency corresponding to the half of the peak value of the curve is a half-power point, and the half-power point is substituted into a damping ratio calculation formula to obtain the damping ratio of the bridge. And substituting the damping ratio, the vibration mode and the inherent frequency into a vibration mode scaling coefficient calculation formula to obtain the vibration mode scaling coefficient of the bridge. In the bridge operation modal parameter identification process, the problem that an excited autopower spectrum ill-conditioned condition is difficult to invert is solved, and the identification precision of the bridge modal parameters is improved.

The application discloses a vertical super-precision machine intelligent servo control method based on dynamic parameter identification and relates to the technical field of servo control. The method comprises the following steps: arranging a first sensing device on a key motion shaft of a target device, so as to acquire vibration data of the motion shaft and obtain vibration information items; and arranging a second sensing device on an oil stone unit, wherein the second sensing device is a pressure and temperature integrated sensing device, so as to acquire real-time pressure data and temperature data of the oil stone and obtain pressure information items and temperature information items. According to the application, low-damage combinations of different oscillation frequencies and feed speeds are preferentially selected as the device to be executed through combined damage sequencing, and the update information of the vibration information items, the pressure information items and the temperature information items is acquired in a deep level, so that the initial information items are secondarily adjusted, thereby realizing the oil stone driving control based on the dynamic parameter identification of the combination of the vibration data, the temperature data and the pressure data.

A method and a system for providing a combination of optimal and stable instances are disclosed. The method includes receiving a configuration information of an application for execution; identifying parameters related to the application of user; identifying a set of optimal instances based on the identified parameters; fetching a data of historical spot instance(s) from a host platform; predicting a stability score for each of the optimal spot instances based on at least the data of the historical spot instance(s); predicting an intermediate set of optimal and stable spot instances from the at least one optimal spot instance based on the stability score of the optimal spot instances; and predicting the combination of optimal and stable instances, based on at least on a cost factor and based on at least one of the intermediate set of optimal and stable spot instances, and a set of optimal on-demand instances.

The invention discloses an automatic building modeling method based on an unmanned aerial vehicle image, and belongs to the technical field of data processing. Comprising the following steps: acquiring a geographic range and a modeling precision requirement of a target area, and acquiring an initial position and a flight state parameter of an unmanned aerial vehicle; determining a data acquisition priority of each area, and generating a preset flight path of the unmanned aerial vehicle in combination with the geographic range and the data acquisition priority; controlling the unmanned aerial vehicle to sequentially move to each target acquisition area according to a preset flight path, and performing image acquisition on the target acquisition areas; performing preliminary noise elimination, distortion correction and image enhancement processing on the to-be-processed image data; creating a four-dimensional space-time index structure containing three-dimensional space information and time dimension; combining information of different scales by using an attention mechanism to form comprehensive building characterization, and identifying the change of the building along with time in real time; the detection sensitivity parameters are automatically adjusted according to the building change condition and the current environment condition, and the significant change part is identified.

According to the real bridge steady-state resonance damper effect evaluation method and device and the readable storage medium, technical means such as excitation and sensing optimization, input reconstruction, closed-loop control, parameter identification, contribution decomposition and multi-path checking are systematically fused; the problems that an existing method is low in signal-to-noise ratio, high in model dependence, complex in calculation, difficult to maintain a resonance state, poor in evaluation result reliability and the like are effectively solved, and the method has higher recognition precision and reproduction practicability in a thousand-grade damping real bridge scene, is not sensitive to frequency drift and interface installation, and is easy to implement. The energy source of damping improvement can be defined, the interface amplitude sensitive energy consumption characteristic can be quantified, real bridge traceable reliable data support is provided for damper model selection, parameter optimization and pavement interface process review, and the requirements for efficient, stable and high-precision evaluation of the effect of the real bridge damper in the technical field of civil engineering structural dynamics and tests are met.

This invention discloses a bolt loosening location method and system based on environmental vibration and multi-scale PNN. The location method includes the following steps: S1: Establishing a refined finite element simulation model of the transmission tower and dividing it into multi-scale hierarchical substructures; S2: Simulating bolt loosening conditions in the finite element simulation model and extracting operational modal analysis results; S3: Calculating the frequency change ratio and modal compliance residual matrix norm, and constructing a multi-source damage-sensitive feature vector after weighted fusion; S4: Constructing and training a probabilistic neural network with an adaptive smoothing factor adjustment mechanism; S5: Acquiring on-site environmental vibration data and identifying modal parameters; S6: Extracting and calculating the measured data of the tower using the random subspace identification method, and inputting it into the trained probabilistic neural network to obtain the location results of the main bolt loosening area. This method achieves accurate bolt loosening location through scientific substructure division and multi-source fusion strategies, providing a reference for the safe operation and maintenance of tower bolts.

The application provides an active mutual exclusion method and system for identifying entry based on a subway gate, and the method specifically comprises the following steps: S1, at time t, obtaining two-dimensional code data submitted by a passenger in front of a gate, performing parameter identification processing on the two-dimensional code data to obtain two-dimensional code parameters and corresponding blocking parameters; S2, discriminating the blocking parameters submitted by the passenger, if the blocking parameters are face blocking parameters, going to step S3, if the blocking parameters are two-dimensional code blocking parameters, going to step S4; S3, performing ride identification on the passenger according to the two-dimensional code parameters of the passenger; S4, obtaining face data of a passenger in front of a gate within a preset time period including time t, and performing ride identification on the passenger according to the face data.

This invention relates to the field of geotechnical engineering investigation and undisturbed sampling technology, and discloses a method for accurately obtaining undisturbed soil samples from boreholes guided by seismic parameters. The method includes the following steps: S1, Exploration data acquisition and identification of key seismic layers: Acquire geotechnical engineering investigation data of the target site. Based on the soil physical and mechanical parameters in the geotechnical engineering investigation data, identify and locate at least one key seismic soil layer that has a controlling effect on the site's seismic response. By constructing a multi-parameter fusion rule base for identifying key seismic soil layers, the automatic identification and location of liquefaction layers, weak layers, and wave velocity interface layers that have a controlling effect on the site's seismic response are achieved. Based on the identification results, the sampling location is dynamically planned to ensure that the sampling range fully covers the top, bottom, and middle sections of the key layer, avoiding the omission of key features due to sampling location deviations in traditional methods. This ensures that the obtained undisturbed soil samples can truly reflect the engineering characteristics of the site's weak points in seismic resistance.

The invention discloses a target export function parameter generation method and device, electronic equipment and a storage medium. The method comprises the following steps: determining an initial parameter structure through an assembly instruction of a target dynamic link library; generating random parameter data based on the initial parameter structure; recording an instruction coverage rate in a process of simulating and executing an export function based on the random parameter data; determining target parameter data of which the instruction coverage rate meets a preset condition; executing an optimization operation based on the target parameter data to determine a parameter value distribution range used for correcting the initial parameter structure; and correcting the initial parameter structure based on the parameter value distribution range, and generating a target derivation function parameter. A dynamic execution process combining static analysis and optimization operation is realized, the problems that effective input is difficult to generate and the instruction coverage rate is low in unsigned information DLL parameter identification are solved, and efficient and accurate identification of the derived function parameter structure is realized.

The invention discloses a bridge damping ratio rapid identification method based on a static detection vehicle, and relates to the technical field of bridge structure vibration tests.The method comprises the steps that the detection vehicle statically stops at the midspan position of a bridge to be detected, the bridge is driven away after being excited by a passing vehicle to vibrate, and the acceleration response of the detection vehicle in an attenuation section is collected; establishing an attenuation section dynamic model of the detection vehicle-bridge coupling system; constructing a two-degree-of-freedom joint state model, and internalizing an axle interaction force into a linear combination of system state variables; an effective attenuation section is extracted through signal preprocessing, and a dual anti-noise mechanism of a signal-to-noise ratio adaptive weight and a Huber weight is introduced; a section two-stage parameter identification strategy is adopted, the frequency and modal quality are locked firstly, and then the first-order damping ratio of the bridge is identified through fine scanning. The method only needs a single detection vehicle and an acceleration sensor, does not need to arrange a sensor on a bridge body, is high in anti-interference performance, is high in recognition precision, and is suitable for the rapid inspection of small and medium span bridges.

The application discloses an ancient building anti-seismic performance evaluation system and method, comprising: a multi-modal information acquisition terminal arranged at a key structure point of an ancient building to be evaluated, which is used for acquiring a primary information set containing structure geometric appearance data, environmental micro-vibration response data and component physical damage state data; the multi-modal information acquisition terminal is adopted in the application, so that multi-dimensional data such as geometric appearance, damage state, dynamic response and environmental parameters can be synchronously collected, and the limitation of single data is avoided; through a Bayesian parameter identification algorithm and a mortise and tenon joint rotational stiffness dynamic degradation function, a digital twin model is accurately corrected, so that the model is highly fitted with actual working conditions, the error between a calculation frequency and a measured frequency is controlled within 3%, and the vibration mode coincidence degree is not less than 0.9; compared with a traditional experience evaluation method and an ideal model simulation method, the evaluation accuracy is greatly improved.

The application relates to a power distribution network topology and parameter identification method under a partial node unobservable condition, and belongs to the technical field of intelligent power distribution networks; the method comprises the following steps: constructing a deep and shallow neural network model and introducing a virtual node to represent an unobservable area; dividing observable and partial observation subgraphs through sparse training; performing Bayesian randomization modeling on network weights, virtual voltages and topology variables, and introducing prior information; performing particle inference by adopting a Stein variational gradient descent algorithm, outputting edge existence probability and parameter confidence intervals; constructing a reinforcement learning search module based on a high-confidence edge set to optimize virtual node connection; finally, a connected topology and line parameters are generated, and a topology risk index and a parameter uncertainty index are output; the application can realize probabilistic identification of the topology and the parameters under the partial node unobservable condition, and improves the reliability, robustness and engineering applicability of the identification result.

The invention belongs to the technical field of unmanned aerial vehicle design, and particularly relates to an open-loop state large-angle-of-attack range longitudinal aerodynamic coefficient identification calculation method, which comprises the following steps of: comparing simulation data with test data in an actual flight test, calculating an error sum of squares of the simulation data and the test data, and minimizing the error sum of squares; dividing the whole attack angle range into a plurality of sub-intervals, respectively obtaining the optimal aerodynamic parameters of each sub-interval based on the minimum sum of squares of errors, inputting each optimal aerodynamic parameter into the flight simulation model, comparing the optimal aerodynamic parameters with test data for verification, and executing the next step when the difference value between the optimal aerodynamic parameters and the test data is within a set range; and drawing an aerodynamic characteristic curve according to each optimal aerodynamic parameter, verifying the applicability of the flight simulation model, and optimizing the robustness of the flight simulation model. The method is suitable for longitudinal aerodynamic coefficient parameter identification of different types of unmanned aerial vehicles, the parameter identification efficiency is improved, and meanwhile the applicability of the model under the complex flight working condition is enhanced.

The invention discloses a marine test parameter identification method for a high-speed moving ship, which comprises the following steps of: 1, integrating a plurality of inertial sensors through an attitude sensing module, and establishing a kinematics relation equation set among an angle, an angular velocity and an angular acceleration corresponding to three-degree-of-freedom rotating motion; 2, constructing a state estimation framework with fault detection capability, analyzing an actual observation value of the sensor and a Kalman filtering predicted value in real time based on a Kalman filtering algorithm to generate a residual sequence, and realizing diagnosis and identification of a sensor fault through statistical test and logical judgment; step 3, carrying out smoothing processing on the sea test data after fault cleaning by adopting a spectral analysis method, and eliminating measurement noise and data delay; and 4, based on the processed high-quality data, establishing a motion equation of the high-speed moving ship, performing linearization processing, converting the motion equation into a discrete ARMAX model structure, and executing ship system model parameter identification by using a generalized least square method.

The invention relates to the technical field of bridge aerodynamic characteristic parameter identification, and provides a bridge two-dimensional section flutter derivative intelligent identification method, a medium and electronic equipment. The method comprises the following steps: acquiring two-dimensional section information data of a bridge; constructing a bridge two-dimensional section finite element model; obtaining a free vibration time sequence of the two-dimensional section of the bridge at a certain wind speed before the flutter critical wind speed; establishing a double-branch hybrid network, and constructing a neural network-based two-dimensional bridge section flutter derivative recognition model by adding Scanlan flutter derivative physical theory constraints in a loss function; and carrying out flutter derivative identification on the free vibration time sequence of the two-dimensional section of the bridge at the wind speed. According to the method, through network structure design and physical constraint of the loss function, it is ensured that the recognition result conforms to a Scanlan flutter theory framework, the problem of physical interpretability of neural network output is solved, and a realizable data-physical fusion normal form is provided for aerodynamic parameter recognition of the two-dimensional section of the bridge.