615 results about "Descent algorithm" patented technology

The invention relates to the technical field of mobile energy storage vehicle energy management, and discloses a mobile energy storage vehicle energy management method based on an intelligent algorithm, and the method comprises the steps: collecting the charging and discharging rate of a battery pack, the environment temperature, the power grid load fluctuation and other multi-dimensional energy state data; a heterogeneous layered architecture of edge computing nodes, a cloud collaboration platform and a vehicle-mounted control terminal is constructed, energy partitions are divided according to peak valleys of a power grid, and low-delay response, global optimization and local closed-loop control modes are configured; fusing the multi-source heterogeneous energy data streams and removing abnormal values; a dynamic optimization decision model is constructed through a battery degradation model and a power grid supply and demand balance equation, and a multi-parameter collaborative constraint relation is solved through simultaneous solving of a multi-target iteration solver and a parallel gradient descent algorithm; and generating a three-level adaptive response instruction sequence of local battery overload alarm, regional power grid frequency modulation early warning and global energy scheduling imbalance pre-judgment based on constraint boundary triggering conditions. According to the method, the accuracy, the collaboration and the robustness of energy management are improved.

The invention discloses a large language model discrete cue word search method and device, and aims to solve the technical problem of overhigh cue word optimization calculation overhead caused by an existing discrete cue word search technology. The method comprises the steps of obtaining training prompt words and training sentences, updating an initial parameter matrix of an initial strategy model by adopting a preset parameter matrix updating function according to the training prompt words, and determining an intermediate strategy model; reasoning according to the training sentence by adopting an intermediate strategy model and a preset generative language model to generate a plurality of disturbance discrete cues and a plurality of reasoning results; based on a preset gradient calculation formula, performing iterative optimization on the intermediate parameter matrix of the intermediate strategy model by adopting an elite coordinate descent algorithm according to the plurality of disturbance discrete cues and the plurality of reasoning results, and determining a target strategy model; and generating a target discrete cue word based on the target strategy model.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention relates to a true orthophoto generation method based on a three-dimensional Gaussian model, a storage medium and equipment, and aims to solve the problems of low image quality, low generation speed, detail loss and the like in the prior art. The method comprises the following steps: firstly, acquiring image data through an unmanned aerial vehicle or aerial photography, and extracting a camera attitude and sparse point cloud by using a motion recovery structure (SfM) technology; secondly, constructing a three-dimensional Gaussian model based on the sparse point cloud, and performing iterative training through top view orthographic projection in combination with a gradient descent algorithm; in the training process, a densification strategy, a point deleting strategy, a blocking strategy and an image pyramid strategy are innovatively introduced, so that the detail expressive force and the overall quality of the image are remarkably improved. Specifically, according to the densification strategy, fine detail reconstruction is achieved by dynamically increasing Gaussian ball density, and according to the point deletion strategy, rendering efficiency is improved and computing resource allocation is optimized by eliminating redundant Gaussian balls. The image pyramid strategy generates a multi-level visual effect through multi-scale training, and the blocking strategy improves the reconstruction precision through local optimization. Finally, on the basis of the trained three-dimensional Gaussian model, real-time generation of a high-quality true orthophoto in a large-scale scene can be realized. The method has remarkable advantages in the aspects of efficiency, precision and practicability, provides important technical support for the fields of geographic information systems, urban planning, disaster monitoring and the like, and has wide application prospects.

The invention discloses a joint test system, a silicon carbide metasurface grating detection method and a silicon carbide metasurface grating detection device, and belongs to the technical field of semiconductor detection. A synergistic excitation signal is generated by regulating a light source and a microwave source and is applied to the silicon carbide metasurface grating to be measured to form a light intensity and microwave field interference pattern. The method comprises the following steps: synchronously acquiring data through multiple channels, constructing a multi-dimensional matrix through time alignment, marker feature extraction and coordinate mapping, extracting defect features to establish a reference model, calculating deviation degree to judge quality, and carrying out abnormal point screening, candidate region construction and coupling verification on a defect grating to divide a potential defect region. For a potential defect area, excitation parameters are dynamically adjusted through a gradient descent algorithm, and defect grades are divided by combining the characteristic defect degree and the area. According to the invention, the problems of insufficient single-mode detection, low multi-mode alignment precision and blind adjustment of excitation parameters are solved, cross-band cooperative detection and intelligent grading are realized, and the detection reliability is improved.

The invention discloses a tensor depth semi-supervised learning method for high-dimensional small sample data classification. The tensor depth semi-supervised learning method comprises the steps of preprocessing original high-dimensional small sample data; constructing a deep neural network comprising a feature extraction module and a classifier module; constructing a second-order similarity matrix and a third-order similarity tensor based on the low-dimensional embedding representation obtained by pre-training; combining the second-order similarity matrix and the third-order similarity tensor to construct an objective function containing multi-order smooth constraints; then, taking the low-dimensional embedded representation obtained by pre-training as input, performing iterative optimization on the target function by adopting a gradient descent algorithm through a label propagation network formed by a full connection layer, and generating a pseudo label; and inputting the original high-dimensional small sample data, the low-dimensional embedded features and the pseudo labels into the deep neural network, iteratively updating the network in a semi-supervised mode until convergence, and outputting a final prediction result. According to the method, more accurate label propagation is realized, and the semi-supervised classification precision is improved.

The invention discloses a carbon emission intelligent prediction system based on a neural network, and relates to the technical field of carbon emission prediction. The system obtains historical energy consumption and carbon emission data of a target enterprise, performs feature extraction and dimension reduction processing, and constructs an energy consumption data set and a carbon emission data set. A complex nonlinear relation between energy consumption data and carbon emission data is captured through a Siamese network, potential features are extracted through a double-branch structure, and an association degree matrix and nonlinear features are output. And performing time sequence modeling on the correlation degree matrix and the nonlinear characteristics through a long short-term memory network, capturing a time sequence dependency relationship between carbon emission and energy consumption, and predicting a future carbon emission value of the target enterprise. Through a gradient descent algorithm, according to an error between a predicted value and an actual value, a parameter weight of the model is optimized. Accurate carbon emission prediction is provided for a target enterprise, and the enterprise is assisted to make a more scientific decision in the aspects of carbon emission management, energy conservation and consumption reduction.

The invention belongs to the technical field of federated learning privacy protection, and particularly relates to a heterogeneous federated learning adaptive privacy protection method and device based on momentum optimization. The method comprises the steps that feature extractor parameters broadcasted by a central server are received, the local gradient of a current iteration round is calculated based on a local data set, and a momentum mechanism is introduced into local gradient updating for a classifier part; a gradient descent algorithm is applied, and local model parameters are updated through the local gradient obtained through calculation; for the feature extractor part, calculating a Fisher information matrix; performing normalization processing on the Fisher information of each layer, and screening strategies; and then differential privacy processing is carried out, the global model parameters are uploaded to a central server for parameter aggregation, and updated global model parameters are broadcasted back to each local client for next round of training. According to the method, the problems of negative influence of a differential privacy mechanism on model training, slow model convergence and poor privacy protection effect are solved.

The invention discloses a dynamic self-adaptive recommendation strategy optimization method for a business handling failure scene, and relates to the technical field of business handling recommendation and intelligent decision making, and the method comprises the steps: firstly collecting various types of data of a whole business handling process, and guaranteeing the integrity and real-time performance at a frequency of 100 milliseconds per time; a decision tree and Bayesian network fusion algorithm is used for attribution, and direct and indirect reasons are clarified; integrating data to construct a user portrait, and mining potential and subsequent demands; generating a recommendation scheme set based on attribution and portraits, and adjusting priorities and forms in combination with scene features; feedback data is introduced, a strategy weight is optimized by using a gradient descent algorithm, and the scheme is updated regularly; a multi-dimensional index weighted evaluation effect is set, and emergency optimization is carried out if the evaluation result does not reach the standard; and establishing a distributed strategy library, and reusing the optimal strategy of the similar scene by using a K-nearest neighbor algorithm. According to the method, failure reason accurate positioning and personalized recommendation are realized, the recommendation effect is continuously optimized along with data accumulation, and the method is adaptive to multiple service types and user groups.

The invention provides a slope displacement monitoring data processing system based on an unmanned aerial vehicle laser radar, and relates to the technical field of data processing, and the system comprises the steps: carrying out the spatial interpolation processing of a topographic feature data set, and constructing a digital topographic surface model; the displacement field calculation module is used for performing iterative optimization based on a digital terrain surface model through spatial similarity analysis and fusion with a gradient descent algorithm, calculating a slope surface displacement vector field, identifying a potential sliding surface and a deformation abnormal region, and generating a displacement field calculation result; and the evaluation module is used for inputting a displacement field calculation result into a risk evaluation model and carrying out slope stability quantitative evaluation through a multi-source data fusion analysis platform. According to the invention, the practicability and operability of the monitoring result are improved.

The invention relates to the technical field of GIS (Geographic Information System) equipment denoising, in particular to a GIS local interference source denoising method, which comprises the following steps of: constructing a hybrid architecture of a quantum convolutional neural network and a quantum recurrent neural network, extracting features by using a variable component sub-circuit as a convolution kernel in the quantum convolutional neural network, processing time sequence data by using quantum bit entanglement in the quantum recurrent neural network, and extracting a local interference source in the quantum recurrent neural network; normalization, coding and enhancement are carried out on electrical, electromagnetic and other multi-source data, a quantum stochastic gradient descent algorithm is used for training, a multi-modal fusion network based on an attention mechanism is also constructed, features of all modals are extracted through an independent convolutional layer, then splicing and weighted fusion are carried out, and finally a quantum reinforcement learning environment is constructed. An intelligent agent strategy network and a reward function are designed based on a quantum neural network, parameters are adjusted according to quantum Q learning, quantum calculation, deep learning and a multi-modal fusion technology are integrated, the GIS local interference source denoising effect is effectively improved, and stable operation of equipment is guaranteed.

The invention belongs to the technical field of signal synchronization, and discloses a multi-camera anti-shake time sequence synchronization control system and method based on an FPGA. The method comprises the following steps: taking an FPGA as a core, generating an FSYNC signal as a camera exposure reference, and obtaining a delay value through a timestamp and differential measurement; acquiring I MU data and obtaining a global motion vector through a gradient descent algorithm; analyzing by combining the two to obtain phase deviation and feeding back the phase deviation to the FPGA, and adjusting the FSYNC signal by combining the global motion vector by the FPGA according to the phase deviation; according to the method, nanosecond timestamp recording is realized through a homologous clock reference and hardware-level signal binding mechanism, and the limitation of dependence on static time difference compensation in the prior art is broken through in combination with global motion vector analysis and dynamic phase offset adjustment.

The invention discloses a fault detection method and system for a fan frequency converter based on big data, and relates to the technical field of fault detection, and the method comprises the steps: arranging a sensor group, and obtaining the real-time operation data of the fan frequency converter; performing entity identification on the historical fault description data to obtain associated parameters of fault types; assigning an initial weight to the associated parameter of each fault type; calculating a real-time deviation ratio, a fault prediction accuracy error and a statistical deviation; calculating a dynamic adjustment coefficient obtained by real-time feedback of each sensor parameter; calculating an error index of each parameter, and optimizing a dynamic adjustment coefficient through a gradient descent algorithm; and calculating a comprehensive fault index by adopting the optimized dynamic adjustment coefficient, and establishing a hierarchical early warning mechanism. The technical problems that potential faults of the frequency converter are difficult to accurately and timely detect in the prior art, so that the operation efficiency of a fan is reduced, even serious consequences are generated, and normal production and operation are influenced are solved.

The invention relates to a data-driven lithium ion battery state prediction method fusing physical information, and belongs to the technical field of batteries. The method comprises the steps that S1, a lithium ion battery multi-working-condition operation database is established, and battery current, terminal voltage and operation temperature data are collected through charging and discharging experiments; s2, a lithium ion battery state prediction model fusing a physical model and a neural network is constructed, physical-prediction joint loss of the model is calculated, the model is trained through a gradient descent algorithm, and physical information is fused into the model through a mathematical relationship between equivalent circuit model parameters and neural network intermediate variables and the joint loss of the model; and S3, inputting real-time battery state data based on the trained model, and predicting a battery state value at the next moment, including SOC or SOT and the like. According to the invention, accurate prediction of the state of the lithium ion battery driven by data fused with physical information can be realized.

The invention discloses a complex geological weak surrounding rock tunnel micro-disturbance green blasting construction method which comprises the following steps: acquiring multi-source data through a sensor and geological survey equipment, and extracting a standardized feature set; and constructing a blasting parameter and surrounding rock response prediction model by using a random forest algorithm, inputting explosive load and hole net parameters, and outputting predicted surrounding rock deformation and vibration values. And if the vibration value exceeds the environment-friendly threshold value, adjusting the parameter by adopting a gradient descent algorithm until the requirement is met. And in combination with real-time monitoring data, the surrounding rock response is dynamically updated by using a Kalman filtering algorithm. If the overexcavation probability exceeds the threshold value, the parameters are further optimized through a genetic algorithm. And finally, optimizing resource allocation through a linear programming algorithm, and determining a final blasting scheme. Intelligent regulation and control of blasting parameters of the weak surrounding rock tunnel are achieved, vibration and deformation are effectively controlled, the overexcavation risk is reduced, the construction efficiency and quality are improved, and a new technical scheme is provided for similar projects.

The invention discloses a method and system for testing the electrical performance of a semiconductor chip, and the method comprises the steps: building a mapping relation between dielectric loss and loss frequency through obtaining the dielectric loss data, internal material characteristics and dynamic load power factors of the chip under each frequency band, recognizing a loss abnormal region, and analyzing the influence of the loss abnormal region on the performance of the chip; correcting test data by adopting a minimum mean square error algorithm, and optimizing the impedance of the power supply network and the dynamic switching characteristic of a functional module; calculating the breakdown voltage distribution of each layer in the chip, identifying a high-voltage risk region, optimizing the voltage distribution by using a gradient descent algorithm, and dynamically adjusting the working voltage and current; power factor fluctuation is analyzed through a sliding window algorithm, an abnormal area is identified, a power factor is optimized through a PID algorithm, and the working state and transient response of a chip in a dynamic load are improved. According to the method, the problems of nonlinearity of high-frequency dielectric loss, non-uniform breakdown voltage distribution and unstable power factor response under a dynamic load are solved, and comprehensive and accurate evaluation and optimization of chip performance are realized.

The invention provides an atmosphere data assimilation method and device, equipment and a storage medium. Relates to the technical field of computer science and atmospheric science fusion. The method comprises the following steps: adding a cost function, priori estimation and the gradient of the cost function into a gradient-based descent algorithm, adding posteriori estimation, and constructing a four-dimensional variation and ensemble Kalman filtering combined model based on Bayesian priori information; and carrying out data assimilation on atmosphere data based on the four-dimensional variation and ensemble Kalman filtering combined model. According to the method, an artificial intelligence technology is combined, especially deep learning and a Bayesian optimization adaptive algorithm are adopted, a traditional data assimilation algorithm is enhanced and optimized, and the method can be widely applied to prediction of extreme weather events such as high temperature and heat waves. Through assimilation of historical data and real-time observation data, the prediction precision of the future climate change trend is significantly improved, and more powerful support is provided for extreme weather early warning and decision making.

The invention relates to a cross-domain voice classification method and device based on feature decoupling and multi-task learning. The method comprises the following steps: firstly, acquiring a multi-data-domain voice file, and preprocessing the multi-data-domain voice file to obtain a cross-domain voice classification data set; then, constructing a cross-domain voice classification model which comprises a voice feature encoder module, a data domain classification module, a supervised comparative learning module and a multi-task classification module; then, a joint optimization loss function is constructed based on data field classification loss, supervised contrast learning loss and task classification loss, and a gradient descent algorithm is adopted to train and optimize the cross-domain voice classification model based on the cross-domain voice classification data set and the joint optimization loss function; and finally, inputting to-be-classified voice into the trained cross-domain voice classification model to obtain a voice corresponding category. The discrimination ability and generalization ability of the model in a cross-domain scene are significantly improved, so that the model still maintains high classification precision in a complex multi-source voice environment.

The invention provides a multi-modal data feature alignment and optimization system and method based on an FPGA, and the system comprises an input buffer module which is used for receiving multi-modal data, carrying out the data processing, and optimizing the buffer depth configuration based on the modal number and the data rate; the space-time perception scheduler is used for extracting space-time characteristics of the multi-modal data and fusing the space-time characteristics according to dynamic weights; the self-adaptive matching kernel is used for mapping the multi-modal feature codes to a unified semantic space and ensuring semantic consistency; the reconfigurable feature compression unit is used for dynamically adjusting a compression ratio through tensor CP decomposition and controlling a reconstruction error based on a rank parameter; a self-calibration optimization mechanism is adopted, feature alignment errors are monitored in real time, mapping matrix parameters are dynamically adjusted through a gradient descent algorithm, and calibration frequencies are switched according to data distribution changes. Through the FPGA pipeline architecture and self-adaptive resource allocation, the calculation delay is reduced, and the throughput is improved.

The invention discloses an intelligent aviation fuel optimization method and related equipment, and the method comprises the steps: constructing an aviation fuel consumption prediction model which is used for predicting the fuel consumption of each leg, and providing basic data for the subsequent refueling optimization calculation; a mathematical optimization method is adopted, the minimum fuel cost and the minimum fuel consumption serve as optimization objectives, and the optimal refueling amount of each leg is calculated; iterative optimization is carried out through a gradient descent algorithm, and an optimal refueling scheme is solved on the premise that preset constraint conditions are met. According to the method, a feasible fuel optimization scheme can be provided with higher precision and lower calculation cost by combining data-driven intelligent prediction and a mathematical optimization algorithm. Compared with a traditional method, the method can adapt to changes of different air routes, weather and market environments, and an airline company can make an aviation oil filling plan more flexibly and efficiently. The method can be widely applied to the aviation oil management field.

The invention discloses a single-bit image recovery method and device, a terminal and a medium, and relates to the field of image processing. The method comprises the following steps: performing single-bit quantization on a to-be-recovered image by adopting a non-zero quantization threshold, and determining an initial image quantization model; replacing a sign function in the initial image quantization model with a smooth hyperbolic tangent function, performing low-rank matrix decomposition on the to-be-recovered image, and determining a target image quantization model; determining an initial optimization model according to the truncated least square function and the target image quantization model; a semi-quadratic minimization method is adopted to convert the initial optimization model, and a target optimization model is determined; solving the target optimization model by adopting a near-end block coordinate descent algorithm to obtain each low-dimension sub-matrix and a noise matrix; and determining a reconstructed image by using each low-dimensional sub-matrix. The problem that in the prior art, quantization noise cannot be effectively restrained, and therefore image restoration precision is affected is effectively solved.

The present invention relates to the technical field of natural ventilation in buildings, and in particular to a computationally assisted decision-making method and system for climate-adaptive building cavity design. The method comprises: using a Delaunay triangulation method to generate an initial mesh, using a Laplace operator-based mesh refinement method to adaptively refine the initial mesh, so as to obtain an adaptive mesh system, wherein the adaptive mesh system is used for dynamically adjusting the mesh density of conditional PINNs; constructing a multi-task learning framework within the conditional PINNs, wherein the multi-task learning framework is used for jointly predicting a plurality of physical field variables within the conditional PINNs; and iteratively training the conditional PINNs, and using a gradient descent algorithm to minimize a loss function until a predetermined number of training iterations or loss convergence is reached, thereby generating a key physical field variable prediction model in building cavity design. The present invention can implement efficient and accurate prediction of key physical quantities in building cavity design, and can be adapted to different building layouts and functional space characteristics.

The invention discloses an automobile part assembly precision intelligent compensation method and a self-adaptive regulation and control system, and relates to the field of intelligent compensation, and the method comprises the steps: synchronously collecting part geometric parameters, tool poses and environment data, and constructing an associated data matrix; analyzing data by using an improved random forest-attention model, and outputting a deviation factor contribution degree sequence; a compensation calculation model is designed accordingly, initial compensation amounts are generated in a segmented mode in combination with a precision margin threshold value, and correction is conducted through historical data similarity matching; selecting an execution path according to the compensation amount and the core deviation type; an actual precision value is obtained through laser detection after assembly, and a compensation error is calculated; based on an error triggering model optimization mechanism, model parameters are iteratively updated by using a gradient descent algorithm, and the deviation identification and compensation precision is improved. The method has the advantages that the model attribution deviation is improved, the precise compensation amount is calculated in combination with historical data, flexible execution, real-time monitoring and model self-optimization are matched, and the assembly precision and the production efficiency are efficiently improved.

The invention relates to the field of twitter recognition, in particular to a twitter classification method based on harmonic enhancement and time-frequency semantic joint modeling, which comprises the following steps: collecting twitter samples and carrying out noise reduction and standardized preprocessing, carrying out multi-scale convolution operation on Mel spectrograms by utilizing a layered acoustic encoder, extracting time-frequency features in combination with a channel attention mechanism, and classifying twitter classification results. The method comprises the following steps of: generating adaptive position codes through a dynamic time-frequency joint coding module, carrying out time-frequency mode modeling by combining a global-local interaction mechanism, introducing a semantic fusion module which comprises a frequency band pyramid unit, a harmonic enhancement unit and a time-frequency gating unit, realizing dynamic weighted fusion of multi-layer features, and carrying out time-frequency mode modeling through a global-local interaction mechanism. And inputting the fusion features into a classification layer, training a network by adopting a cross entropy loss function and a gradient descent algorithm, and outputting bird categories through a full connection layer, thereby solving the key problems of insufficient description of a non-stationary time-frequency mode, insufficient modeling of a harmonic structure, reduction of recognition performance in a complex noise environment and the like in the prior art.

The invention discloses a control method and system of a mechanical arm, electronic equipment, a medium and a program product. The control method comprises the steps that the target pose of an end effector under world coordinates and forward kinematics information of the mechanical arm are obtained; according to the first joint angle of the mechanical arm, the forward kinematics information and the first current pose of the end effector, the second current pose of the end effector under the world coordinates is determined; determining a loss function, wherein the loss function comprises a first loss function corresponding to the difference between the target pose and the second current pose; and based on the loss function, the first joint angle is updated through a gradient descent algorithm to control the mechanical arm to move. The mechanical arm is controlled to move without collecting training data in advance, and the calculation cost is reduced.

The invention provides a 6D attitude rapid estimation method and device for a target object, and relates to the field of electrical digital data processing in the technical field of machine vision and perception, and the method comprises the steps: obtaining an RGB image and a depth image of the target object, inputting the RGB image into a segmentation model, and obtaining a binary mask of the target object; acquiring a camera internal reference matrix, and constructing a curved surface point cloud matrix of the target object according to the depth image, the binary mask and the camera internal reference matrix; constructing a cubic package of the target object, and constructing a fitting penalty function according to the curved surface point cloud matrix and the cubic package; according to the curved surface point cloud matrix, the cubic package and the fitting penalty function, gradient vectors of the center point and the Euler angle of the target object are obtained through calculation; and performing iterative solution on the gradient vectors of the central point and the Euler angle through a gradient descent algorithm to obtain optimal solutions of the central point and the Euler angle, and taking the optimal solutions of the central point and the Euler angle as the 6D attitude of the target object.

The invention provides an iron tower voiceprint intelligent detection method and system, and belongs to the voiceprint detection technology. The method comprises the following steps: firstly, outputting a sweep frequency signal in a specific frequency range, collecting iron tower vibration response data, extracting candidate frequency through spectral analysis, and dynamically adjusting excitation frequency by using a gradient descent algorithm; hardware filtering is carried out on collected sound signals, frequency band signals related to excitation frequency are reserved, and space beam forming is carried out through a microphone array to enhance iron tower voiceprint signals. Separating iron tower vibration components from the mixed signals by utilizing independent component analysis, performing time-frequency analysis, extracting an energy ratio of a specified frequency band by adopting wavelet packet transformation, and performing deep learning processing in combination with a one-dimensional convolutional neural network to generate an energy ratio and zero-crossing rate feature vector; and finally, inputting the feature vectors into a support vector data description model, setting an initial threshold value and dynamically adjusting the initial threshold value, thereby realizing graded judgment of bolt looseness and improving detection efficiency and stability.

The invention discloses a multi-dimensional training method and device for a support vector machine, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the discretization of a training sample data set, and obtaining a discretized data set, the discretized data set comprises a plurality of different attributes, and each attribute corresponds to a plurality of feature vectors; calculating a classification contribution parameter of each attribute feature vector to obtain a plurality of classification contribution parameters; performing data mapping on the plurality of classification contribution degrees by using a kernel function to obtain a target function; and optimizing and training the objective function by using a gradient descent algorithm to obtain a support vector machine model. According to the method, different dimension data are mapped through the kernel function, the data gain weight can be determined, the linear separable effect of the mapped dimension data can be improved, and then the classification and analysis capability of the SVM can be improved.

The invention discloses a fuel cell flow channel-electrode coupling design and attenuation optimization method based on machine learning, and the method employs a machine learning algorithm to construct a data driving model based on a fuel cell physical model, and achieves the rapid prediction and feature quantitative analysis of the performances of a cell under different flow field and electrode coupling structures. A gradient descent algorithm is further combined, multi-target and multi-parameter flow field structure optimization is carried out, key parameters in the reaction process are accurately regulated and controlled, and the stability and long-term performance of the fuel cell are improved. According to the method, coupling comprehensive optimization can be accurately and comprehensively carried out on the runner and electrode parameters, and a multi-target comprehensive optimization result which is difficult to realize by a traditional optimization method is obtained. By locally regulating and controlling flow field parameters and optimizing reactant concentration uniformity, substance exchange efficiency and temperature and humidity control, attenuation of the fuel cell is effectively improved, and the overall performance of the fuel cell is improved.