140 results about "Orthogonal decomposition" patented technology

The invention provides a compressor flow field reduced-order modeling method and device based on POD and deep learning, and relates to the technical field of compressor flow field modeling, and the method comprises the steps: obtaining three-dimensional transient flow field data of a compressor flow field region under a plurality of working condition parameters, and constructing a flow field data set containing a plurality of time snapshots based on the three-dimensional transient flow field data; obtaining target flow field snapshot data of the compressor based on the flow field data set; performing intrinsic orthogonal decomposition on the target flow field snapshot data, and obtaining a dimension-reduced time coefficient matrix based on the decomposed data; and constructing a training set based on the working condition parameters and the dimensionality-reduced time coefficient matrix, and training a pre-constructed neural network prediction model by using the training set to obtain a target prediction model. According to the compressor flow field reduced-order modeling method, the target prediction model integrated with the flow physical law can be trained and constructed, and the prediction task under the new working condition can be completed by adopting the target prediction model.

The invention relates to a thermal coupling over-reduced order prediction method and system for a multi-component assembled nonlinear system. The method comprises the following steps: collecting multi-scale physical field data; constructing an intrinsic orthogonal decomposition basis function space of a temperature field and a stress field, and establishing a double-field coupling constraint equation; constructing contact thermal resistance parameterized proxy models of a cylinder contact area, a bolt area and a free deformation area by adopting a domain discrete empirical interpolation method; constructing a parametric intrinsic mode tensor network to obtain a decline model which is used for realizing real-time reconstruction of a mode basis function through acquired tensor slices in an online stage; performing dynamic inversion based on a modal basis function reconstruction result, and outputting a predicted transient displacement field, a predicted temperature gradient field and a predicted contact stress field; and obtaining real-time parameters, and calculating a residual error with a corresponding prediction result so as to dynamically update the primary function and interpolation point distribution. Compared with the prior art, the real-time prediction of the transient thermal coupling of the multi-component contact system is realized on the premise of ensuring the precision.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention relates to a trajectory prediction model robustness enhancement method based on dynamic subspace projection decomposition. Comprising the following steps: firstly, extracting hidden layer semantic features containing historical tracks and map topology through a multi-modal feature encoder; secondly, constructing a dynamic routing mechanism based on scene self-adaption, and calculating projection weights of input features on a plurality of expert subspaces; then, executing truncation projection operation based on orthogonal decomposition, retaining core semantics located in a low-dimensional space, and filtering out adversarial disturbance located in an orthogonal complementary space; and finally, introducing a feature consistency constraint training mechanism, taking the reconstructed features of the clean sample as anchor points, and compulsively aligning the purified features of the confrontation sample with the anchor points. Compared with the prior art, the method has the advantages that the robustness of the model in white box gradient attack, black box query attack and physical semantic deception scenes is remarkably improved through feature purification of a physical level and structured consistency constraint, and the prediction reliability of the automatic driving system is ensured.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

The invention discloses a monitoring equipment fault rapid positioning method based on causal diagram reasoning, and the method comprises the following steps: building a thermal diffusion model composed of equipment nodes and links through collecting the topological structure and operation data of a monitoring system; normalizing and fusing the operation deviation information into a node abnormal energy value, and establishing an abnormal energy propagation equation; when an alarm is triggered, collecting an energy change sequence of each node to generate a space-time energy distribution matrix, and reversely solving a thermal diffusion equation to calculate a disturbance source item; performing clustering backtracking on disturbance source items, determining fault root cause nodes, and comprehensively evaluating confidence; and when multi-source abnormity exists, thermal field orthogonal decomposition is executed, and model parameters are adaptively updated according to field results. According to the method, abnormal propagation of monitoring equipment is modeled into a thermal diffusion process, and reverse solution and adaptive calibration are combined, so that rapid, explainable and high-precision positioning of fault root causes in a complex monitoring network is realized.

The invention discloses a flow field prediction-oriented full-process automatic joint reduced-order modeling method, which comprises the following steps of: specifying a target physical field parameter space, and randomly generating a sample space according to a Latin hypercube sampling method; constructing a full-process automatic simulation tool chain, driving target physical field numerical calculation and generating a training data set; carrying out singular value decomposition-based intrinsic orthogonal decomposition on the output physical field data, and only retaining first r main feature components to construct a reduced-order data set; constructing a multi-input multi-output full-connection feedforward neural network, and modeling and training a nonlinear mapping relation between input parameters and reduced-order features; new working condition parameters are input, reduced-order features are predicted through the trained neural network, distribution of a target physical field is reconstructed according to a singular value decomposition reduction matrix, and more flexible and reliable technical support is provided for reducing the training cost of a reduced-order model and improving simulation efficiency.

The invention discloses a mountainous area wind field downscaling optimization method based on high-precision simulation, and relates to the technical field of meteorological numerical simulation, and the method comprises the steps: obtaining the terrain elevation data and meteorological monitoring data of a target mountainous area, recognizing slope abrupt change points, constructing a spiral sampling path, calculating a slope change value, determining the distance between sampling points, and generating target sampling data; performing orthogonal decomposition on the sampled data to obtain a frequency component, calculating a surface fluctuation coefficient, and constructing a boundary disturbance equation to obtain surface stress distribution; calculating an airflow motion state based on surface stress distribution, solving a vorticity equation to obtain vorticity characteristics of a leeward area, and calculating airflow motion correction parameters; and carrying out downscaling iterative operation on the corrected parameters and the meteorological monitoring data to generate target area wind field data with hectometer-magnitude spatial resolution. The simulation precision of the local wind field under the complex terrain condition is improved.

The invention relates to the technical field of digital twinning, physical modeling and multi-source data fusion, and provides a digital twinning dynamic construction method based on multi-source data fusion and physical simulation. The method comprises the following steps: acquiring a multi-source heterogeneous data stream from a preset sensor array, a numerical simulation result and a historical database, identifying a key feature mode of a dominant physical process in the multi-source heterogeneous data stream, acquiring a key feature mode time-varying physical field evolution rule corresponding to the key feature mode by using a time sliding window and a forgetting mechanism, extracting a low-dimensional sparse characteristic parameter set reflecting dynamic behaviors from a high-dimensional observation space, and constructing a reduced-order proxy model by adopting Gaussian process regression, a neural network proxy model or an intrinsic orthogonal decomposition combined interpolation technology; and receiving a corresponding real-time observation data stream to establish a full-closed-loop feedback link from model prediction, high-fidelity solution verification to observation data correction in combination with the reduced-order proxy model so as to complete the construction of the digital twin.

The invention discloses a summer rainfall sub-season prediction method and system fused with multi-scale deep learning, and the method comprises the steps: collecting multi-source weather forecast data and observation data, and carrying out the empirical orthogonal decomposition of the observation data, and obtaining a rainfall main mode and a mode sequence; performing multi-scale signal extraction on the observation data, and performing correlation analysis on the observation data and the modal sequence to obtain respective weight fields; constructing and training a deep learning model fusing a multi-pole attention mechanism and time sequence decomposition; inputting the forecast data into the trained model to carry out transfer learning, and optimizing the model; and substituting forecast data of preset time into the trained model to generate a high-quality summer rainfall sub-season forecast product. According to the method, the synergistic effect of sea, land and gas and the interaction of multi-scale signals are fully considered, the model is constructed based on an artificial intelligence method and a numerical model forecasting product, the sub-season forecasting skill of summer rainfall is effectively improved, and the method plays an important role in disaster prevention and reduction.

The invention relates to the technical field of multi-degree-of-freedom motion control and self-stabilization platforms, in particular to an error compensation control method for a self-stabilization holder under multi-branch-chain redundancy collaboration, which comprises the following steps of: acquiring a pose and a disturbance signal based on branch chain physical topology, constructing a disturbance propagation path, and identifying a main error source branch chain and a secondary error branch chain; performing orthogonal decomposition on the error component of the main error source branch chain, and mapping the error of each secondary error branch chain to a unified error coordinate system; extracting time domain features of the direction and amplitude error, and generating a corresponding compensation signal; distributing a branch chain weight according to the disturbance intensity ratio, and constructing a redundant branch chain cooperative control instruction; synchronously driving each branch chain to execute compensation, and verifying whether the attitude error is converged or not; and if not, correcting the error identification parameter and the disturbance model, and feeding back to the control cycle. According to the invention, multi-branch chain high-dynamic coordination and error self-learning compensation can be realized, and the attitude stability and anti-disturbance capability of the holder are improved.

The invention discloses a truss type load-bearing structure time domain dynamics topological optimization method and system based on an intrinsic orthogonal decomposition method, and relates to the technical field of truss type load-bearing structure topological optimization. The method is provided for solving the problems of high calculation power and the like caused by large-scale space-time response iteration of original transient problems and intensive calculation of dual problem sensitivity analysis in the prior art. According to the method, a local geometric control strategy for the cross interference problem of rod piece units is introduced, and a transient dynamics full-order topological optimization model of the truss structure is established through an equal geometric stiffness diffusion method. Based on an intrinsic orthogonal decomposition method, a transient dynamics reduced-order model of a truss structure is constructed, a reduced-order model base is updated in real time through an incremental singular value decomposition method, and then efficient solving of a transient dynamics equation and an adjoint equation in sensitivity analysis is achieved. And updating an iterative design variable by adopting a penalty function method, and seeking the optimal layout design of the truss structure. The invention provides a time-domain dynamics topological optimization method of the truss type load-bearing structure based on an intrinsic orthogonal decomposition method, and the method can be used for efficiently solving the dynamics layout optimization problem of the truss structure under the action of a transient load.

The invention relates to the technical field of fluid mechanics modeling and aerodynamic acoustic analysis, in particular to a spectral orthogonal decomposition-based separated flow wall surface pressure pulsation dominant structure modeling method, which comprises the following steps of: acquiring a whole flow field and / or wall surface unsteady pressure data of a separated flow under a preset working condition; performing spectral orthogonal decomposition on the unsteady pressure data, performing frequency domain decoupling on multi-scale features, and extracting feature values and spatial dominant feature modes under each feature frequency; constructing a complex wave packet physical parameterized model, fitting the spatial dominant feature modals, and compressing the spatial dominant feature modals into sparse physical parameter vectors after nonlinear regression solution; and reconstructing a wall surface pressure pulsation dominant component based on the sparse physical parameter vector and the characteristic value, and carrying out at least one of flow mechanism analysis and pneumatic order reduction modeling according to the dominant component. Therefore, the problems of low reconstruction precision and the like caused by the fact that asymmetric evolution and variable acceleration convection of a large-scale structure in separation flow wall surface pressure pulsation cannot be accurately represented in a frequency domain in related technologies are solved.

The invention discloses a citrus lossless identification method and system based on near infrared spectrum characteristic decoupling, and the method comprises the steps: obtaining near infrared spectrum data of a citrus sample, and coding the near infrared spectrum data to a submerged space to obtain spectrum characteristic representation; mapping the pooled features to a discrete citrus mode codebook by using a vector quantizer, and extracting mode features; performing multi-scale convolution operation on the spectral feature representation, and extracting fine-grained spectral features; performing orthogonal decomposition on the fine-grained spectral features into personalized features and invalid features through orthogonal decomposition; and according to the extracted pattern features and personalized features, citrus category identification is realized. The citrus lossless identification system integrates and realizes spectral feature representation, quantization, feature extraction and category identification functions. According to the method, the mode features and the personalized features of the citrus are extracted, the accuracy rate reaches 85.24% in 80 types of citrus identification tasks, and the method has a wide application prospect.

The invention discloses a grid-connected inverter fusion control method based on voltage and current source time sharing, and relates to the technical field of electric power automation, and the method comprises the steps: carrying out the two-phase rotating coordinate system conversion of power grid coupling data, and generating a voltage-current joint component; performing power error analysis on the voltage-current joint component, outputting a power grid state vector, performing modal decomposition on the power grid state vector, obtaining a dominant oscillation mode matrix, performing Lyapunov exponent calculation on the dominant oscillation mode matrix, forming a stability pre-judgment mark, and inputting the stability pre-judgment mark into a time-sharing fusion decision network model to obtain a time-sharing fusion decision network model. The characteristic coupling layer carries out cross physical quantity correlation, and the stability evaluation layer carries out stability boundary calculation and outputs a voltage-current source control signal. Through the intrinsic orthogonal decomposition algorithm and the time-sharing fusion decision network model, the response speed and the fusion control capability of the grid-connected inverter are improved.

The invention relates to the technical field of wind power plant power control, in particular to a wind power plant dynamic power distribution optimization method based on projection dimensionality reduction. The method comprises the following steps: collecting a high-dimensional original operation data stream of a wind power plant, and preprocessing to output a high-dimensional state vector; extracting a dominant mode matrix through an intrinsic orthogonal decomposition algorithm, and projecting a high-dimensional state vector to a low-dimensional space; future modal coefficient evolution is predicted through an autoregression prediction model, and a low-dimensional rolling optimization problem is constructed and solved through a sequential quadratic programming algorithm; reconstructing the optimal low-dimensional modal coefficient vector into a target power instruction of each fan, and issuing and executing the target power instruction; and updating the dominant mode matrix through an incremental singular value decomposition algorithm and correcting parameters of the autoregressive prediction model. According to the method, projection dimensionality reduction from high-dimensional data to a low-dimensional space is realized through intrinsic orthogonal decomposition, and the problem of low optimization solution efficiency caused by high data dimensionality of a traditional method is solved.

The invention relates to the technical field of circuit load state monitoring, and discloses a directional drilling pipeline surface coating damage characteristic analysis method based on data inversion, which comprises the following steps: injecting a dual-frequency composite excitation signal into a pipeline transmission loop; locking a current phase, collecting a magnetic flux gradient vector and performing orthogonal decomposition; according to the method, impedance distortion caused by medium heterogeneity is eliminated through the pilot frequency response difference, the problem of misjudgment caused by the fact that traditional detection depends on medium isomorphism hypothesis is solved, and the detection accuracy is improved. And accurate identification of transmission load leakage impedance abnormity is realized.

The invention discloses a maneuvering target tracking method and system based on orthogonal decomposition and parameter adaptation. The method comprises the following steps: acquiring an initial state of a target; a self-adaptive interactive multi-model filtering framework is provided, complex three-dimensional space motion of a target is orthogonally decomposed into a horizontal plane and two perpendicular planes which are perpendicular to each other, and complete description and tracking are carried out on any space maneuver; and designing an online parameter adaptive mechanism, intelligently adjusting the smooth strength of the quasi-uniform B spline according to the real-time maneuvering mode probability output by the IMM filter, robustly identifying the turning angular velocity in each plane through a multi-section geometric average strategy based on the optimal smooth trajectory, and dynamically feeding back the identification result to the corresponding collaborative turning model. According to the method, a complex high-dimensional nonlinear tracking problem is converted into a plurality of parallel and adaptive low-dimensional problems for solving, and finally, high-precision, fast-convergence and strong-robustness tracking of the high-maneuvering target is realized.

The invention belongs to the technical field of hydraulic engineering digital twinning, and provides an arch dam static response rapid calculation method which comprises the following steps: sampling and simulating based on a finite element model in an offline stage to obtain a displacement field snapshot matrix, extracting a dominant mode through intrinsic orthogonal decomposition to construct a reduced-order subspace, and calculating a mode coefficient; and training a radial basis function neural network to establish a nonlinear mapping relation by taking the working condition parameters as input and the modal coefficient as output, and finally, inputting the target working condition parameters into the trained network to predict the modal coefficient in an online stage, and quickly reconstructing a complete displacement field response through linear combination with the POD modal. The method achieves the quick and accurate prediction of the static physical field of the arch dam, and remarkably improves the calculation efficiency of the displacement field of the arch dam.

The invention relates to the technical field of thermal management, in particular to a liquid cooling plate flow resistance characteristic evaluation method and device, a medium and equipment. According to the method, the calculation load is remarkably reduced by converting the flow data into the reduced-order model by utilizing the intrinsic orthogonal decomposition. This can achieve faster analysis without sacrificing accuracy, so that the design can be evaluated and optimized in real time. Moreover, different from the traditional method which may depend on a simplified model or empirical correlation, the method based on the intrinsic orthogonal decomposition can capture a main flow mode, namely a dominant mode, which causes resistance. These modes reveal critical flow characteristics, such as high shear stress, vortex formation and flow separation, which are crucial for understanding the root cause of flow resistance. The capability of quantifying and isolating the important flow structure can deeper understand how the specific flow behavior affects the resistance, so that guidance is provided for the optimization design of the geometrical shape of the liquid cooling plate.

The invention provides a cavitation flow field reconstruction method based on sparse perception and an unsupervised learning method, and the method comprises the steps: firstly, carrying out the numerical simulation of cavitation flow around a specific type of hydrofoil, considering the unique characteristics of a cavitation flow velocity field, executing the intrinsic orthogonal decomposition, and representing the intrinsic orthogonal decomposition as a representation form of the superposition of an average velocity field and a pulsation velocity field; a fluid velocity field modal library is constructed on the basis; and then an unsupervised clustering algorithm and sparse data sampling are executed to extract data samples, and fluid velocity field reconstruction is completed by using the velocity field modal library and a small number of data samples. Through verification and comparison of reconstruction effects under different sparse sampling strategies, high-fidelity reconstruction of a complex cavitation flow structure can be finally realized, and compared with the prior art, accuracy and robustness are remarkably improved.

The invention discloses a micro-channel heat exchanger topological optimization design method, which comprises the following specific steps of: 1, executing key working condition parameter sampling in a design space, constructing a topological optimization mathematical model, carrying out iterative computation, and establishing a topological optimization database comprising a topological field and corresponding temperature field, flow field and pressure field data; 2, realizing unified order reduction of a topological field and each physical field by adopting intrinsic orthogonal decomposition, and converting a complex high-dimensional structure into low-dimensional modal representation; 3, constructing a multi-task learning neural network, taking the multi-target constraint parameters as input, taking the topological field modal coefficient and each physical field modal coefficient as output, and adopting a PyTorch deep learning framework to train a topological structure generation model; and 4, a model is generated based on the trained topological structure, the high-resolution topological structure can be predicted within second-level time, and the thermal-fluid performance of the high-resolution topological structure can be synchronously fed back. The method has a remarkable calculation speed-up ratio and good engineering mobility, and an efficient solution is provided for micro-channel heat dissipation design of a high-power electronic device and a new energy system.

The invention discloses an extra-high voltage transformer bushing transient temperature rise rapid prediction method, which comprises the steps of constructing a simulation model of an extra-high voltage transformer bushing, and simulating and extracting global node temperatures and corresponding working condition parameters of a bushing profile at multiple moments to form a data set; selecting global node temperature to perform orthogonal decomposition, and selecting first r-order modals to construct a modal matrix according to cumulative energy proportion truncation; projecting global node temperature in the data set to a modal space according to a modal matrix to obtain a modal coefficient vector as an output sequence, and performing sliding window on working condition parameters in the data set to form an input sequence; constructing a long-short-term memory network model, optimizing network hyper-parameters of the long-short-term memory network model, and training the model by adopting an input sequence and an output sequence to obtain a time sequence mapping model; and acquiring working condition parameters of the extra-high voltage transformer bushing in a to-be-measured time period, inputting the working condition parameters into the time sequence mapping model, predicting a modal vector at a future moment, and reconstructing transient temperature field distribution of the extra-high voltage transformer bushing in combination with inversion of a modal matrix.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.

The application discloses a kind of offshore wind farm automatic power generation control flow field dynamic modeling methods, comprising the following steps: data sampling is carried out using CFD simulation, obtains data set;Data is collected and arranged;Eigen-orthogonal decomposition is used for order reduction processing;Offshore wind farm automatic power generation control flow field dynamic model based on yaw, variable pitch, speed regulation process and its disturbance process is constructed.The application can effectively overcome the shortcomings that the data set obtained by high-precision wind farm flow field dynamic simulation is large in structure and difficult to apply directly, and the dynamic reduced-order model of the wind farm flow field obtained can greatly compress the data structure, retain the main characteristics of the flow field, and has high wake prediction accuracy.

This invention relates to the field of engineering structure performance prediction and optimization technology, specifically disclosing an engineering full life cycle performance prediction and optimization system. The system collects time-series data of load, acceleration, and strain of engineering structures under extreme loads; constructs a physical consistency mapping operator based on real-time input data and finite element prior information, outputting a predicted response field; performs intrinsic orthogonal decomposition on the finite element prior information to extract the basis vector and integral point subset, trains the time-series evolution rule of the basis coefficients, and outputs low-dimensional basis coefficient prediction values; compares the real-time input data with the predicted values, and triggers online basis assimilation update when the deviation exceeds a threshold, assimilating the low-dimensional basis coefficients to output a corrected stiffness field; substitutes the corrected stiffness field into the time-series evolution rule to extrapolate the future response field, generating a smooth control force command for the active mass damper; this invention achieves real-time closed-loop optimization control of engineering structures under extreme loads.

The application relates to the technical field of industrial equipment state monitoring, and particularly discloses a fault prediction and health management system based on an industrial robot, which collects robot benchmark operation data and real-time operation data, and constructs a benchmark data set containing individual identity labels; multi-domain feature extraction and weighted fusion are performed on the data, a high-dimensional feature space is obtained through phase space reconstruction; the high-dimensional features are decomposed into mutually orthogonal individual attribute subspaces and degradation state subspaces by using an orthogonal subspace learning algorithm, and pure benchmark degradation features are obtained; a health index is constructed based on the benchmark degradation features, and a segmented continuous degradation model is established; real-time features are projected into the degradation state subspace to obtain real-time degradation features, which are input into the degradation model to invert the remaining service life and output graded early warning information; the application effectively suppresses false abnormal alarms by stripping individual difference interference through orthogonal decomposition of the feature space, and improves the cross-device generalization capability and prediction accuracy.

The invention relates to the technical field of vehicle noise vibration recognition, and discloses a road noise recognition method and system based on principal component analysis, and the method comprises the steps: constructing an excitation matrix through four-wheel vibration signal time domain alignment, separating independent excitation components of a suspension subsystem through orthogonal decomposition, calculating the energy ratio of each component in a target frequency band, and carrying out the calculation of the energy ratio of each component in the target frequency band; a physical transmission path corresponding to the dominant component is identified, structural parameters are adjusted in a targeted manner to reduce transmission efficiency, and precise treatment of road noise is realized; the system comprises a vibration sensing module, a sound pressure sensing module, a signal preprocessing module, a matrix decomposition module, a path identification module, a parameter optimization module and a control execution module. According to the method, four-wheel vibration signals are decoupled through orthogonal decomposition, a correlation model of excitation components and suspension / tire parameters is established, a structural parameter optimization scheme is generated, multiple modules are integrated to realize road noise closed-loop treatment, a quantitative evaluation system is constructed in combination with multi-dimensional data, and the recognition precision and optimization efficiency are improved.

The invention discloses a geomagnetic gradient tensor depth representation learning method and system, and belongs to the technical field of information processing, and the method comprises the steps: obtaining a geomagnetic three-component data matrix of a magnetic object, and building an original gradient tensor data set; generating a gradient matrix based on the original gradient tensor data set, and performing orthogonal decomposition on the gradient matrix to obtain mutually independent rotation isovariant features and direction sensitive features; inputting the rotation isovariant features and the direction sensitive features into a rotation isovariant convolutional neural network for feature extraction; constructing a geometric constraint loss function including an angle constraint loss function and a direction constraint loss function, and optimizing the training network; the geomagnetic gradient tensor data is subjected to deep representation based on the trained network, the innovative gradient matrix orthogonal decomposition technology solves the problem that rotation invariance and direction sensitivity are difficult to consider at the same time, the feature representation accuracy is improved by about 30-40%, the processing speed is improved by 2-3 times, and the anti-interference capability is remarkably enhanced.

This application relates to a method, system, device, and medium for measuring pressure distortion in an aero-engine inlet. The method includes: acquiring typical flow field categories and corresponding intrinsic orthogonal decomposition modal basis functions (IOD) of the target aero-engine inlet; based on optimization objectives and constraints, solving for the measurement positions of the aerodynamic interfaces of the target aero-engine inlet using IOD to obtain a pressure sensor measurement position arrangement scheme; acquiring sparse pressure data collected by each pressure sensor deployed according to the pressure sensor measurement position arrangement scheme; determining the current typical flow field category based on the sparse pressure data, and reconstructing the sparse pressure data using Gappy orthogonal decomposition based on the IOD corresponding to the current typical flow field category to obtain the global total pressure distribution; and calculating the performance parameters of the inlet pressure distortion based on the global total pressure distribution. This method enables accurate evaluation of pressure distortion characteristics.