110 results about "Support vector machine classifier" patented technology

The invention relates to the technical field of high-voltage circuit breaker fault diagnosis, and discloses a multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method. The method comprises the following steps: adaptively optimizing variational mode decomposition parameters by adopting a particle swarm optimization algorithm, and accurately decomposing an original vibration signal; performing noise dominant and fault feature dominant classification on the intrinsic mode function based on permutation entropy; aiming at the two types of modes, respectively taking signal-to-noise ratio maximization and kurtosis maximization as targets, and implementing differential wavelet threshold denoising; after reconstructing the signal, extracting an energy entropy, a singular value entropy and a power spectrum entropy to form a multi-dimensional feature vector; and inputting the data into a support vector machine classifier subjected to particle swarm optimization hyper-parameter for state diagnosis. According to the invention, through full-chain collaborative optimization, the accuracy and robustness of fault diagnosis in a strong noise environment are significantly improved.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.

The invention provides a method for measuring surface defects of automobile stamping parts by laser scanning, and belongs to the technical field of part surface defect detection.The method includes the steps that a high-curvature area and a low-curvature area are marked, a self-adaptive viewpoint planning path is constructed based on curvature distribution, dense sampling is adopted in the high-curvature area, and the low-curvature area is marked; a laser scanning device is driven to collect three-dimensional point cloud data, anisotropic thermal diffusion defect enhancement processing is performed on point cloud, a sliding window is adopted to calculate a multi-scale box counting fractal dimension to generate a fractal dimension field distribution diagram, a suspected defect area with the fractal dimension deviating from a normal reference value is identified, and a composite feature vector is extracted; a support vector machine classifier is input to judge a real defect, a defect depth value is corrected by adopting multispectral illumination to measure a bidirectional reflection distribution function parameter and combining with a phase measurement profilometry aiming at the condition of a large incident angle, a defect distribution diagram marked with defect information is generated, and the problem of low surface defect detection precision of a high-curvature area is solved.

The invention discloses a multi-modal data fusion and fault diagnosis method, and belongs to the field of transformer partial discharge fault diagnosis. According to the method, for the problems of false alarm and missing alarm caused by data isolation and lack of effective integration in partial discharge diagnosis of the transformer, acoustic, infrared and visible light multi-mode data are synchronously collected, pixel-level space alignment is carried out based on feature point matching, time sequence synchronization is achieved through hardware trigger signals, and the fault diagnosis accuracy is improved. Multi-level fusion diagnosis of a data layer, a feature layer and a decision-making layer is adopted, including channel superposition to form a fusion diagnosis image, voiceprint features, temperature rise features and arc light or corona features are extracted and input into a feature fusion model to obtain an associated feature vector, decision fusion is performed through a support vector machine classifier and a D-S evidence theory, and a decision-making result is obtained. And outputting a final diagnosis conclusion, thereby realizing accurate and reliable diagnosis of the partial discharge fault of the transformer.

The invention belongs to the field of man-machine interaction, and particularly discloses a multi-scene switching method, system, equipment and medium based on a song ordering table, and the method comprises the steps: collecting the behavior data of residence time, song ordering frequency and interaction intensity of a user in a virtual environment through a sensor, and processing the behavior data through a long and short term memory network, obtaining a user dynamic preference vector; matching is carried out according to the user dynamic preference vector and a preset scene type library, if the matching degree is higher than a threshold value, it is judged that the current scene preference is stable, otherwise, it is judged that potential switching intentions exist, and potential switching intention probability distribution is obtained through the judgment; after the probability distribution of the potential switching intention is obtained, a support vector machine classifier is adopted to train association features among multi-scene data; the invention aims to solve the problems of resource loading delay and unsmooth interface switching caused by rapid change of user scene preference in a virtual environment in the prior art.

The invention discloses a distribution hidden danger assessment and prediction method based on a natural time domain and a fuzzy rough set, and belongs to the technical field of operation and maintenance of power grid equipment. According to the method, based on collected multi-source monitoring signals of leakage current, induction current, temperature and the like, adaptive signal preprocessing is carried out by adopting a method of combining empirical mode decomposition (EMD) and sample entropy, effective mode components are effectively extracted, and noise interference is suppressed; natural time domain analysis is introduced on the basis of a traditional time domain, an event sequence is constructed, dynamic features are extracted, and a hidden danger feature data set with time sequence evolution information is formed; further performing unsupervised attribute reduction on the high-dimensional features by using a fuzzy rough set theory, removing redundant information, and retaining key discrimination features; and finally, identification and trend prediction of wiring hidden danger types are realized through a support vector machine (SVM) classifier. According to the method, the accuracy and robustness of hidden danger identification are improved, and effective technical support is provided for intelligent operation and maintenance of power distribution.

The invention relates to the field of railway fault diagnosis, in particular to a railway switch fault diagnosis method, device and equipment, and the method comprises the following steps: S1, collecting an original vibration signal of a switch, and decomposing the original vibration signal of the switch through variational mode decomposition to form a mode signal; s2, extracting multi-scale entropy features from the decomposed modal signal, wherein the multi-scale entropy features are respectively a multi-scale wavelet coherent weighted permutation entropy, a multi-scale weighted diversity entropy and a multi-scale Mel spectrogram fusion entropy; s3, the multi-scale entropy features are respectively used for training a support vector machine classifier, so that a plurality of independent diagnosis models are constructed, and a plurality of diagnosis results are obtained; and S4, integrating a plurality of diagnosis results through a decision fusion strategy of hard voting to obtain the fault type of the switch.

The invention discloses a soft soil area earth surface deformation identification method and system based on Beidou short message and InSAR cooperation, and the method comprises the steps: collecting high-frequency deformation data through a ground sensor array, obtaining a low-frequency wide-area deformation image through combining with an InSAR satellite, and constructing a multi-source data set through space-time matching; and data missing is filled by adopting an interpolation algorithm, and the data quality is ensured through consistency verification. And a support vector machine classifier is used to identify an abnormal settlement area, and a filtering algorithm is used to predict a deformation trend based on a historical sequence. And when the predicted value exceeds a threshold value, an alarm signal is automatically generated, a thermodynamic diagram is visually rendered through a GIS system, and the high-risk infrastructure is positioned. And finally, the data set is circularly updated through real-time sensor feedback, fusion parameters are iteratively optimized, and hidden deformation dynamic accurate sensing is realized. According to the method, the hidden deformation identification precision is remarkably improved, and efficient and accurate monitoring and early warning support is provided for the safety of infrastructures in the soft soil area.

The invention discloses a vaginal wall mucosa wrinkle degree evaluation method and system based on image processing, and relates to the technical field of medical image processing, and the method comprises the steps: converting a colposcope image from an RGB space to an LAB space, extracting a brightness channel, and extracting a continuous and significant wrinkle contour through an edge detection algorithm; drawing a binary image for a single connected domain, detecting end points by using a sliding window, recording curve coordinates according to an anticlockwise tracking algorithm, and completing boundary extraction by adopting curvature adaptive downsampling; extracting curve characteristics of each fold; based on the curve data set, adopting a support vector machine classifier to carry out model training, and carrying out form classification on the extracted wrinkle curve; extracting comprehensive curve characteristics, and comprehensively describing the richness and the distribution rule of vaginal wall wrinkles; and selecting a typical sample as a centroid, dividing the feature vector of the to-be-evaluated image to the nearest centroid category through iterative calculation, and outputting an evaluation result of the vaginal wall mucosa wrinkle degree.

The invention discloses a battery mechanical damage monitoring method and system in the transportation process of an electric vehicle based on acoustic emission spectrum analysis, and the method comprises the steps: carrying out the multi-scale decomposition of an original signal sequence through wavelet transform, separating out a high-frequency transient component and low-frequency background noise, and obtaining a denoised elastic wave signal; calculating time domain features including peak amplitude and duration according to the denoised elastic wave signal, and combining frequency domain features such as a main frequency component to obtain a comprehensive feature vector; if the peak amplitude of the comprehensive feature vector exceeds a preset threshold value, judging that the event is a potential damage event, and extracting a damage related subset from the feature vector to obtain a damage candidate feature; training the damage candidate features through a support vector machine classifier to obtain damage type labels; and aiming at the damage type label fusion transportation environment data, a sliding window is adopted to analyze and track the signal change trend, and the damage evolution degree is determined. According to the invention, accurate identification, classification and dynamic monitoring of transportation damage of the battery pack are realized.

The invention discloses a medical incorruption risk prevention and control system and method based on multi-source data fusion, and relates to the technical field of information processing.The medical incorruption risk prevention and control method comprises the steps that system operation log data, ICD coding data and medicine catalog data in a hospital system are obtained, standardization processing is conducted on the ICD coding data and the medicine catalog data, and an initial incidence relation is established; constructing a medical encoder, and inputting data to obtain corresponding feature vectors; calculating the association strength of the codes and the drugs, generating an association feature vector, and inputting compliance and non-compliance data into a support vector machine classifier to train an association verification model; inputting system operation log data into the model to obtain an operation matching degree, and if the operation matching degree is lower than a threshold, triggering early warning; and obtaining a continuous operation time sequence of an operator, calculating the deviation between the time interval and the historical average interval, and triggering early warning if the deviation exceeds a threshold value. According to the method, through multi-source data fusion and an intelligent model, accurate prevention and control of medical incorruption risks are realized, and the supervision efficiency is improved.

The invention relates to the technical field of optical fiber sensing, in particular to an OTDR event analysis method and system, electronic equipment and a storage medium. The method comprises the following steps: carrying out de-noising processing on an OTDR original curve to obtain an OTDR curve; acquiring a local curve segment corresponding to each sampling point in the OTDR curve, calculating a local slope value and acquiring a jitter degree; setting a compensation value according to the jitter degree, and constructing a dynamic threshold interval corresponding to the local curve segment based on the compensation value and a preset optical fiber loss value; when the local slope value exceeds the dynamic threshold interval, determining a suspected event starting point from the local curve segment; in the OTDR curve, obtaining a suspected event curve segment by combining the suspected event starting point with a preset length; and performing discrete Fourier transform and normalization processing on the suspected event curve segment to obtain a corresponding frequency domain normalization sequence and obtain corresponding statistical characteristics, and inputting the statistical characteristics into a pre-trained support vector machine classifier to obtain an event analysis result. The method and the device are used for improving the accuracy of OTDR event analysis.

The invention provides a CNN-ISO rolling bearing fault diagnosis method based on double feature selection. Belongs to the field of rotary mechanical equipment fault diagnosis. In the first stage, a convolutional neural network (CNN) is utilized to automatically extract high-dimensional depth features from original vibration signals, subjectivity and complexity of artificial feature construction are avoided, and it is ensured that the feature extraction process is more objective and comprehensive; in the second stage, an improved snake swarm optimization (ISO) algorithm is combined with feature elimination and feature activation, synchronous optimization of feature subset selection and support vector machine (SVM) classifier parameters is realized, efficient global search is carried out in a feature space and a parameter space, and the collaborative improvement potential of optimal feature combination and classifier performance is fully mined. The method is superior to a traditional method in the aspects of accuracy, search efficiency and model lightweight, and can provide a thought for real-time and accurate diagnosis of the early failure of the industrial field bearing.

The invention discloses an intelligent electromagnetic heating safety control system for an oil field, and relates to the technical field of heating control, the system is composed of a plurality of functional modules, and the system comprises a feature extraction module for collecting time sequence data of intelligent electromagnetic heating equipment, including air outlet temperature, pressure and hot air flow, performing phase-space reconstruction on the time sequence data, performing singular value decomposition on the reconstructed time sequence data, and extracting dynamic association features; the data migration module is used for constructing a health model adaptive to a new working condition by adopting a deep migration learning method and combining with the time sequence data under the current working condition based on normal operation data under historical similar working conditions; and the fault prediction and diagnosis module is used for acquiring the dynamic correlation features, inputting the dynamic correlation features into the health model to obtain health feature representation, health degree and fault prediction time, calculating residual errors based on the health feature representation, introducing time-frequency features, and adopting a support vector machine classifier to take the time-frequency features and the residual errors as input.

The invention relates to a high-rise building heat insulation layer hollowing detection method and system based on an unmanned aerial vehicle. The method comprises the following steps: converting point cloud data of a building into a three-dimensional network model by using a Poisson reconstruction algorithm, and generating a spiral scanning unmanned aerial vehicle route; the unmanned aerial vehicle flies according to the route of the unmanned aerial vehicle, collects flight attitude data and reflection echo signals of the laser radar, and suppresses flight jitter interference through an anti-jitter signal processing algorithm; performing time-frequency domain analysis on the radar signals after noise reduction, extracting double-interface reflection coefficient features, inputting the double-interface reflection coefficient features into a support vector machine classifier to identify a hollowing region, and calculating hollowing position coordinates; and generating a three-dimensional thermal image of the building, and outputting a hollowing detection report. Anti-interference processing is carried out through an anti-jitter signal processing algorithm, so that hollowing feature misjudgment caused by jitter can be avoided; the whole process is automatic, and labor time consumption can be saved; automatic, precise and quantitative evaluation of hollowing detection is realized.

The invention discloses an online monitoring method and device for an insulated cable, and relates to the technical field of online monitoring. The method comprises the steps that real-time insulated cable data are collected and preprocessed, and an available data set is formed; calculating the real-time insulation level of the insulated cable by adopting an algorithm combining a leakage current monitoring method and a high-frequency current method; inputting real-time cable environment data based on a Bayesian dynamic linear model, and dynamically generating a defect judgment threshold value; comparing the real-time insulation level with a threshold value, if the real-time insulation level is lower than the threshold value, determining that an insulation defect exists, and positioning a defect area through physical coordinates; and performing feature extraction on the defect area data, inputting a support vector machine classifier model, and outputting an insulation defect type label. According to the invention, through a plurality of links of data acquisition, preprocessing, insulation level calculation, adaptive threshold determination, defect positioning and defect type classification, powerful support is provided for real-time state monitoring of the insulated cable.

The invention provides an enterprise supply chain financial risk prediction method fusing a dynamic knowledge graph and a graph neural network. The method comprises the steps of multi-source data loading and preprocessing, supply chain knowledge graph construction, financial feature extraction, risk label generation and the like. Aiming at the problems that multi-subject information of core enterprises, suppliers, customers and the like in supply chain data is dispersed and is inconsistent with independent financial data structures, the method solves the problems that manual integration is low in efficiency and prone to errors through automatic data cleaning, entity matching and relation mapping, and a unified and structured enterprise supply chain relation knowledge graph is constructed; the limitation of manually designing network features is overcome by utilizing a graph attention network; an effective feature fusion strategy is designed, and joint feature representation with higher discriminative force is formed; and constructing an end-to-end evaluation framework, training classifiers such as a support vector machine to learn the fusion features, realizing accurate and stable classification of high-risk and low-risk enterprises, and providing probability output to enhance result interpretability and decision support capability.

The invention relates to a rail crack identification method and system based on ultrasonic guided waves and a support vector machine. The method comprises the steps that the ultrasonic guided waves are emitted at the rail waist position of a to-be-detected rail through an excitation transducer; receiving transducers arranged at preset intervals are used for collecting the propagated guided wave signals and digitalizing the propagated guided wave signals; preprocessing the digital signal, and extracting a multi-dimensional feature vector containing a time domain feature and a frequency domain feature; the feature vector is input into a pre-training model, a rail health state classification result is output, an alarm is triggered, the pre-training model is based on a support vector machine classifier, and a multi-dimensional feature vector composed of a time domain feature and a frequency domain feature extracted from an ultrasonic guided wave signal of a standard rail sample is used as input data; and taking a preset damage label corresponding to the crack damage position and depth as output data, and training and optimizing a kernel function and a hyper-parameter to obtain the data. According to the invention, a detection blind area can be broken through, real-time on-line monitoring is realized, and the problem of complexity of guided wave signal analysis is solved.

This invention discloses a clutter discrimination and suppression method based on multiple echo features, comprising: acquiring radar front-end clutter and target echo detection video data and dividing it into several connected regions; extracting multi-dimensional features and labeling prior information for each connected region; using the Relief feature selection algorithm to calculate and filter the weights of the multi-dimensional features, removing redundant features to obtain the optimal feature vector; using this feature vector to train a support vector machine classifier, and obtaining the optimal parameter model through cross-validation; acquiring measured clutter data, extracting corresponding features and inputting them into the model; and determining whether to remove clutter and retain targets based on the output. This invention effectively filters out dynamic clutter spots, avoids false deletion of weak targets, significantly reduces the false alarm rate of the system, and alleviates the computational burden of subsequent track processing.

The invention discloses a mining area vegetation degradation identification method based on a remote sensing spectrum. The method comprises the following steps: collecting multispectral image data of a mining area; based on the multispectral image data of the mining area, main spectral feature vectors are extracted by adopting a principal component analysis method, and a vegetation spectral feature set is determined; based on the vegetation spectral feature set, performing health state division on vegetation pixels by adopting a support vector machine classifier to obtain a vegetation degradation classification map; extracting a spatial distribution pattern from the vegetation degradation classification map, calculating an area proportion and a position coordinate of each type of pixels, and determining a spatial distribution feature of a degradation degree; acquiring a multi-temporal remote sensing image sequence according to the spatial distribution characteristics of the degradation degree, analyzing and tracking the change of a classification graph through time sequence difference, and judging a dynamic change trend; driving mechanism indexes are integrated from the dynamic change trend, and a comprehensive monitoring and recognition report is obtained and used for revealing the trend of vegetation degradation in the mining area.

The invention discloses a steel pipe defect detection method based on artificial intelligence, and belongs to the technical field of pipe detection, and the method comprises the following steps: S1, obtaining original data from a steel pipe surface image, extracting initial features through a convolutional neural network, and generating a first feature set; s2, aiming at the first feature set, adopting an adaptive threshold segmentation algorithm to generate a second feature set; s3, according to the second feature set, performing preliminary division on the defect region through a region growing algorithm to obtain a plurality of defect region images; s4, extracting local texture features of each defect area image, and generating a third feature set corresponding to each defect area image; and S5, for the third feature set, performing defect type classification by adopting a pre-trained support vector machine classifier to obtain a defect type classification result. The steel pipe defect detection method based on artificial intelligence solves the problem that the precision and robustness of current steel pipe surface defect detection are difficult to improve.

This invention discloses a method and system for monitoring mechanical damage to batteries during electric vehicle transportation based on acoustic emission spectrum analysis. The method includes: performing multi-scale decomposition of the original signal sequence using wavelet transform to separate high-frequency transient components and low-frequency background noise, obtaining a denoised elastic wave signal; calculating time-domain features, including peak amplitude and duration, based on the denoised elastic wave signal, and combining this with frequency-domain features such as the dominant frequency component to obtain a comprehensive feature vector; if the peak amplitude of the comprehensive feature vector exceeds a preset threshold, it is judged as a potential damage event, and damage-related subsets are extracted from the feature vector to obtain damage candidate features; training the damage candidate features using a support vector machine classifier to obtain damage type labels; and fusing transportation environment data with the damage type labels, using sliding window analysis to track signal change trends and determine the degree of damage evolution. This invention achieves accurate identification, classification, and dynamic monitoring of battery pack transportation damage.

The application provides an enhanced hyperspectral framework method for storing medicinal plant pest classification, comprising: the framework collects near-infrared hyperspectral images of medicinal plant samples, and constructs a dataset containing healthy level, mild infestation level and severe infestation level through controlled artificial infestation to obtain real spectral data as a basis; the framework adopts a Wasserstein generative adversarial network model with gradient penalty to enhance the real spectral data, generate synthetic spectral data to balance the class distribution and increase the sample diversity, wherein the model ensures the fidelity of the generated data through adversarial training; and the framework applies a convolutional neural network classifier, a support vector machine classifier and a random forest classifier based on the enhanced dataset for pest classification, wherein the convolutional neural network classifier achieves the best performance and realizes the accuracy improvement.

The invention relates to an InSAR interferogram quality discrimination method based on deep learning, and the method comprises the steps: carrying out the preliminary decomposition of an InSAR interferogram through a multi-scale wavelet transform method, and carrying out the feature extraction, and obtaining a nonlinear feature vector; carrying out deep learning processing on the nonlinear feature vector by adopting a convolutional neural network to obtain interference weight distribution under multi-factor coupling; based on the interference weight distribution, obtaining superposition influence parameters of topographic relief and atmospheric interference, and determining a quantization level of deviation increase; and performing pattern recognition on the quantization level with the increased deviation through a support vector machine classifier to obtain a stable and corresponding quality judgment result. According to the method, the quality judgment accuracy, stability and robustness under the complex terrain and the atmospheric environment are remarkably improved, the reliability of geological disaster early warning is enhanced, and the method has important practical value.

The invention belongs to the technical field of airport meteorological monitoring, and relates to an airport dominant visibility measurement method and system based on multi-source data fusion, and the method comprises the steps: 1) employing the backscattering coefficient, PM2.5 concentration and relative humidity of a laser radar as feature vectors, and carrying out the weather classification through a support vector machine classifier; 2) under different weather types, using a laser radar to obtain echo signals of the whole airport area, and using a point-type visibility meter to obtain point-type visibility; 3) preprocessing the echo signal and obtaining laser visibility based on the echo signal; 4) performing dynamic weight iterative optimization on the point-mode visibility, the laser visibility and the near visibility under different weather types through a dynamic weight multilayer sensor to obtain final visibility; and 5) performing dominant visibility judgment based on the final visibility, and determining the dominant visibility. Through multi-source data fusion and an intelligent algorithm, the problems of insufficient precision, poor environmental adaptability and low automation degree in the prior art can be solved.

The invention belongs to the technical field of posture recognition, and provides a posture recognition method and system based on identity feature desensitization, a medium and a server, and the method comprises the steps: carrying out the preprocessing of three-dimensional point cloud data containing human body posture information, and carrying out the feature extraction through a deep learning algorithm, and carrying out standardization processing, covariance matrix eigendecomposition and principal component analysis dimension reduction processing on the extracted eigenvector in sequence to obtain a desensitized eigenvector, inputting the desensitized eigenvector into a pre-trained support vector machine classifier, and outputting an identification result including the basic posture of the human body. According to the method, after the original point cloud is converted into the feature vector, the posture data is detected through the SVM classifier, human body joint point recognition and data output are not involved, meanwhile, identity relevance in the point cloud data is effectively eliminated through identity feature desensitization processing, and privacy protection is higher; the deep learning algorithm is adopted to perform feature extraction and posture recognition, so that the recognition accuracy is improved.

The application discloses a kind of fusion uncertainty perception man-machine cooperation implicit sponsorship content detection and intervention method, belong to natural language processing field.It includes: using large language model to extract post text depth semantic embedding and image visual features, and with platform metadata, content quality features and sentiment features to build unified structured feature representation;Through the calibration support vector machine classifier with category imbalance penalty weight, learn the optimal classification hyperplane between implicit sponsorship content and natural content, reliable posterior probability is output using Platt scaling;Based on the mixed query strategy of entropy sampling and bayesian inconsistency, the most informative samples are handed over to manual review under limited labeling budget, and manual feedback is continuously converted into model retraining signal;Posterior probability is converted into probabilistic disclosure label and is shown to users.The application realizes more reliable, more interpretable and more suitable for platform governance scene implicit soft advertising detection and probabilistic disclosure.

This invention relates to the field of automotive parts processing technology, and discloses an automatic feeding control method and system for an automotive parts electrophoresis line based on multiple sensors. The method includes: acquiring initial multi-source data through a sensor matrix; preprocessing the data to obtain a refined feature vector; acquiring feeding deviation values; if the feeding deviation exceeds a threshold, generating a feeding deviation signal; using a support vector machine classifier to classify and predict the set of deviation signal intensity indicators to obtain a classification result of the deviation factors; mapping the classification result to a control strategy library to match dynamic adaptation rules and obtain feeding parameter correction values; if environmental changes dominate the deviation, calculating a first adjustment coefficient using temperature compensation; performing a secondary calibration of the correction value based on the convergence result to generate a feeding command; updating system parameters and determining whether the deviation has been eliminated; and updating model parameters to obtain updated data acquisition rules. This method can achieve accurate identification and real-time correction of feeding deviations, improving control accuracy.

The invention provides a reinforced concrete pipe-jacking hole-entering method suitable for a quicksand layer, and relates to the technical field of pipe-jacking construction. The reinforced concrete pipe-jacking hole-entering method suitable for the quicksand layer comprises the following steps: S1, based on a multi-frequency-band geological radar array, adopting a wavelet transform algorithm to decompose a reflected wave signal, identifying quicksand layer density distribution through a support vector machine classifier, constructing a three-dimensional geological feature matrix, and generating a quicksand layer three-dimensional permeability cloud picture; a multi-frequency-band geological radar array is combined with a wavelet transform algorithm, multi-scale decomposition is performed on reflected wave signals, quicksand layer density distribution features are extracted, a three-dimensional geological feature matrix is constructed, accurate quantitative description of geological parameters is achieved, and compared with a traditional geological exploration method, the permeability recognition precision is improved, and the method has the advantages of being high in practicability and the like. A jacking force-torque prediction model is established by adopting a long short-term memory neural network, and a jacking parameter combination is dynamically generated in combination with multi-objective optimization of a genetic algorithm, so that the matching degree of the jacking speed and the grouting pressure is improved.

The invention relates to the technical field of engineering mechanical transmission control, discloses a loader transmission speed regulation method based on bucket angle change, and aims at solving the problems that a traditional loader transmission system is lagged in speed regulation, large in impact and high in energy consumption. The method comprises the steps that the bucket angle, the engine rotating speed, the gearbox output shaft rotating speed and hydraulic system pressure data are collected in real time; constructing a multi-dimensional working condition feature vector based on the first-order and second-order derivatives of the bucket angle and the hydraulic pressure; a support vector machine classifier is used for recognizing five operation stages of ground cutting, material filling, full bucket lifting, high-position discharging and empty bucket falling; the hydraulic torque converter and the planetary gear mechanism are driven by the gearbox electric control unit to cooperatively act, and the speed ratio deviation is corrected in combination with a closed-loop feedback mechanism. According to the technical scheme, accurate matching of the transmission system and the operation working condition is achieved, fuel consumption and mechanical abrasion are remarkably reduced while the operation efficiency is guaranteed, and the intelligent level and the operation comfort of the whole machine are improved.