1263 results about "Support vector machines svms" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention relates to the field of power equipment, and discloses a power equipment fault early warning system, which comprises a data acquisition module, a data fusion module, a state evaluation module, a trend prediction module, an early warning judgment module and an information interaction module. Key operation parameters are cooperatively acquired through multiple types of sensors, time series data are uniformly calibrated by adopting a timestamp mechanism, the problems of fragmentation of operation state information of power equipment and superposition of acquisition errors are effectively solved, and then feature fusion and dimension reduction compression are performed on high-dimensional heterogeneous data by introducing a principal component analysis and auto-encoder neural network, so that the operation state information of the power equipment is acquired. According to the method, redundant information is eliminated, meanwhile, key discrimination features are reserved, the sensing dimension of the system for the equipment operation state is more comprehensive, the representation capacity is higher, the Bayesian network and the support vector machine are adopted to jointly evaluate the equipment state health level, higher state recognition accuracy is achieved in a dynamic scene, and the method is suitable for popularization and application. And the model generalization ability is enhanced through historical samples, so that the equipment state can be judged more stably.

The invention relates to the technical field of safety monitoring, in particular to a bridge safety monitoring system based on sensor data. The method comprises the steps that a sensor measuring unit monitors key physical parameters of a bridge in real time; the data acquisition and transmission unit reads sensor data and transmits the read sensor data; the data processing and analysis unit extracts key features reflecting the health state of the bridge in the sensor data based on a multi-modal physical perception feature fusion method, identifies an abnormal mode in the sensor data through a support vector machine model, and evaluates the remaining life of the bridge based on a carbonization-corrosion-crack closed-loop feedback control rule; and the information management unit stores the analysis result of the data processing and analysis unit and displays the monitored bridge physical parameters. According to the design of the invention, a multi-modal physical perception feature fusion method is introduced, and adversarial learning, graph wavelet transformation and a three-dimensional convolutional neural network are combined, so that high-precision feature extraction of bridge structure response is realized.

The invention discloses a measurement and control system adaptive adjustment method and system based on deep learning, and relates to the technical field of intelligent regulation and control, and the method comprises the steps: collecting the real-time operation data of a measurement and control system, constructing the real-time operation data into a system state vector, carrying out the feature extraction of a task in the measurement and control system based on the system state vector, and carrying out the adaptive adjustment of the measurement and control system. The method comprises the steps of classifying tasks by adopting a support vector machine, predicting resource demand quantity for different types of tasks, dynamically adjusting task priorities based on the resource demand quantity, generating task priority information, and performing global resource allocation optimization by adopting a genetic algorithm through a macroscopic scheduling layer; real-time scheduling decision making is carried out through the microcosmic scheduling layer by adopting a reinforcement learning model, and a specific resource allocation instruction is generated based on a resource allocation scheme output after optimization of the macroscopic scheduling layer. The utilization rate of system resources is improved, and task response delay is reduced.

The invention relates to the technical field of sound wave measurement, in particular to an ultrasonic image diagnosis system and method.According to the ultrasonic image diagnosis system and method, matching between reflection characteristics and tissue characteristics is made to have the self-adaptive learning ability through a deep neural network, the targeted recognition effect is improved in the aspect of signal classification, and based on the judgment result of the reflection characteristics, the diagnosis accuracy is improved. When boundary partitioning is carried out on a tissue area, the edge structure is judged by using the combination of three parameters of gray range, gradient direction and texture continuity, contour fuzziness caused by single index judgment is avoided, and the boundary partitioning accuracy is improved by extracting a gray distribution center, an amplitude change track and an edge area continuous change sequence. And the region positioning result is input into a support vector machine, accurate recognition of lesion properties is realized according to boundary classification comparison of morphological structure quantitative features and historical benign and malignant feature data, secondary verification of the structural form is performed after the image is formed, and the diagnosis integrity and the judgment confidence are effectively improved.

The invention relates to the technical field of cable fault detection, in particular to a cable fault analysis system based on big data, which comprises a disturbance feature extraction module, a time sequence hierarchy judgment module, a reflection characteristic analysis module, a topological section identification module and a fault path mapping module. According to the method, gradient inflection points are extracted by calculating voltage and current increment included angles at adjacent moments, spatial-temporal characteristics of disturbance signals are correlated, interference of noise to a starting point mark is reduced, time difference is fitted in combination with cable length, deviation over-threshold abnormal points are dynamically filtered, influences of synchronization errors on sorting are eliminated, and fault path mapping stability is improved; the method comprises the following steps: generating characteristic parameters by fusing amplitude values and wave crest intervals of main waves and reflection waves, quantifying amplitude attenuation and phase offset coupling, enhancing the multi-path superposed signal separation capability, judging the reflection characteristic similarity of a physical section based on a Manhattan distance and a support vector machine, and dynamically dividing a fault section in combination with line topology. The problem of fuzzy positioning in a branch parallel scene in a traditional method is solved.

The invention relates to the technical field of sound anomaly detection, in particular to a sound anomaly detection method and device based on a Transform model, equipment and a medium, and the method comprises the steps: collecting a sound signal during the operation of the equipment through a data collection interface, and obtaining an original sound signal; resampling is carried out on the collected sound signals, and normalization processing is carried out on the resampled data; mel-frequency cepstrum coefficient features are extracted from the sound signals after normalization processing, and the sound signals after normalization processing are input into a pre-training module to output high-dimensional features including time sequence and semantic information; splicing the Mel-frequency cepstrum coefficient features with the high-dimensional features to form a comprehensive feature vector; inputting the comprehensive feature vector into a support vector machine model, and performing abnormal sound recognition through a trained classification hyperplane; and detected abnormal information is fed back to the user in real time. Multi-feature fusion enables the model to identify abnormal sound more accurately.

The invention relates to the technical field of textile machinery fault detection, and discloses a spinning machine fault detection method and system based on deep learning, and the method comprises the steps: collecting vibration, temperature and current signals through a plurality of sensors, constructing a data stream after principal component analysis and dimension reduction, employing a variational auto-encoder to reconstruct an error positioning abnormal data segment, and obtaining a spinning machine fault detection result. The method comprises the following steps: extracting local features of a multi-source signal in combination with a convolutional neural network, dynamically distributing feature weights by using an attention mechanism, screening time sequence key features through a forgetting gate, introducing an LSTM network to model a feature sequence dynamic relationship, completing fault category mapping based on a support vector machine, and triggering LSTM secondary classification calibration for samples with insufficient confidence. A closed-loop technical system of data dimension reduction, anomaly detection, feature optimization, time sequence modeling and classification verification is formed, and efficient analysis and accurate recognition of multi-modal fault features under complex working conditions are achieved.

The invention relates to the technical field of defect detection, in particular to a suturing scalpel packaging quality detection method based on optic nerves, which comprises the following steps: collecting suturing scalpel packaging images to perform angular point detection and gradient screening, obtaining alignment parameters under a unified coordinate system, calculating an optical flow displacement initial value and a residual error, and calculating an optical flow displacement initial value; an ROI sub-image is cut to extract a cutting edge contour; an orthogonal projection length is calculated to generate a cutter form mark; texture and shape features are extracted and input into a support vector machine for defect identification; a random forest regression model is called to calculate compensation parameters; according to the method, coordinate mapping is established through angular point detection and gradient screening, displacement is corrected through combination of optical flow residual and depth tracking, a cutting edge contour is extracted through ROI cutting, shape deviation is quantized through orthogonal projection, a classification basis is constructed through texture fusion, defects are identified through a support vector machine, parameters are compensated through random forest regression, and performance is optimized through closed-loop feedback.

The invention discloses a network security early warning method and system based on artificial intelligence, and relates to the technical field of network security early warning and defense. According to the method, multi-dimensional intelligent analysis of network traffic and accurate identification of abnormal traffic modes are realized, the defense concealment is enhanced through a concealed channel coding technology, detection of attackers is avoided, internal association and evolution paths of attack modes are mined by using a dynamic knowledge graph and an attention mechanism, and the attack modes are accurately identified. The method improves the recognition and prediction capability of novel attacks and advanced threats, optimizes a camouflage strategy, combines a lightweight encryption algorithm and distributed execution nodes, efficiently deploys defense actions, evaluates the attack mitigation effect in real time through a support vector machine, dynamically adjusts a defense strategy, achieves the quick recovery of a network state, and achieves the stable operation. And the real-time performance and the automation level of safety response are obviously improved.

The invention relates to the technical field of motor state monitoring and fault prediction, and provides a motor residual life analysis method and system based on a support vector machine, and the method comprises the steps: building a multi-dimensional degradation feature set of a motor, and calculating a health index degradation rate based on multi-source sensor data and a failure threshold; constructing a support vector machine regression model of an adaptive kernel function, predicting a health index change track by using the model and real-time data, and generating a residual life evaluation result if a prediction deviation is within an allowable error range; otherwise, starting an incremental learning mechanism to update the training data set, dynamically adjusting kernel function parameters, and recalculating the trajectory; and if the error requirement is still not met, model regularization parameters are optimized in combination with the working condition data until the residual life evaluation result is converged. According to the method, the accuracy and dynamic adaptability of motor residual life prediction can be improved, and the robustness of the model to complex working conditions is enhanced.

The invention provides an intelligent optimization method and system for tunnel smooth blasting blast holes in layered rock mass, and belongs to the technical field of tunnel engineering and blasting. The method comprises the steps of collecting stratified rock geological data, evaluating and extracting features, constructing a multi-objective optimization function, solving by an intelligent optimization algorithm, performing construction feedback and dynamic optimization and the like. The method comprises the following steps of: acquiring data by using various geological exploration equipment, performing evaluation processing on the data by using a support vector machine model, constructing an optimization function considering multiple indexes, solving an optimal blast hole parameter combination by combining a genetic algorithm and a particle swarm optimization algorithm, and finally feeding back a blasting effect to dynamically adjust an optimization scheme. According to the intelligent optimization method and system for the smooth blasting blast holes of the tunnel in the layered rock mass, the blasting effect can be improved, over-break and under-break and concrete consumption can be reduced, the construction cost can be reduced, the construction safety and efficiency can be enhanced, and remarkable economic and social benefits can be achieved for layered rock mass tunnel engineering construction.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention provides a Transform and CNN fused crack detection and structure evaluation system and method. The system comprises an image preprocessing module, a crack feature extraction module, a crack positioning and classification module, a crack boundary refinement module and a structure integrity evaluation module. Through combination of the CNN and the Vision Transform, local and global features in the image can be extracted at the same time, and the precision of crack detection is enhanced. The dynamic attention mechanism is used for refining fracture boundaries and improving fracture positioning and recognition effects. And the structure health assessment module combines crack information and structure stress analysis, performs structure risk assessment by using a support vector machine or a random forest, and outputs a structure health state and a repair suggestion. The invention further provides an evaluation scheme of the system. The method improves the precision and robustness of crack detection, has higher multi-scale detection capability, noise robustness and real-time performance, and is suitable for automatic monitoring and health management of civil infrastructures.

The invention relates to the technical field of fishing boat monitoring, in particular to an AI camera-based fishing information acquisition system for a lamplight cover net fishing boat, which comprises a reflective point extraction module, a track fitting module, a shielding classification module, an edge redrawing module and a density analysis module. The method comprises the following steps: extracting reflective points on the surface of an underwater fish body, screening a high-brightness area, constructing a gloss response point set, analyzing a motion track of a reflective point group in combination with a multi-frame image sequence, fitting path features by adopting a random sampling consistency algorithm, and identifying a track continuity state and a motion mode change. And performing shielding state classification on the path features based on a support vector machine model, judging extension shielding, cross shielding or boundary separation types, performing direction vector alignment and path matching redrawing on the edge contours of the reflective points according to a classification result, and recovering contour missing caused by shielding. And multi-granularity data support is provided for fish catch statistics by analyzing a brightness change track of a fin ray region in a redrawn edge path.

The invention provides a dance movement image analysis method, system and device based on image time sequence recognition, and relates to the technical field of image processing, and the method comprises the steps: obtaining a dance movement image sequence, and determining the pixel position information of a dancer through image segmentation; calculating an initial displacement track of the dancer according to the change trend of the pixel position information of the dancer; and extracting high-dimensional features of the dancer in the image sequence, and combining the initial displacement track. According to the method, displacement information related to dance movement meanings is obtained through dimensionality reduction and semantic association of the convolutional neural network, and a multi-modal feature matrix is constructed in combination with joint angles and limb posture data. A long short-term memory network is adopted for time sequence modeling, continuity and smoothness of actions are captured, a key displacement mode is extracted through clustering analysis, a dance action characterization model is constructed, finally, a support vector machine is used for classification training, and the performance of the model is optimized by adjusting fusion weight iteration.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention discloses a power transformer arc discharge multi-parameter detection simulation platform and a fault diagnosis method, and relates to the technical field of power system equipment state monitoring and fault diagnosis. The platform comprises a transformer body, a replaceable discharge module, a multi-parameter sensing unit and a signal processing and diagnosis module, and can truly reproduce various typical arc discharge faults of a needle plate, an air gap, a creeping surface, turn-to-turn and the like. The sensing unit is integrated with ultrahigh frequency and ultrasonic sensing probes, high-frequency current and voltage sensors, optical fiber temperature / pressure / strain sensors and the like, so that synchronous acquisition of multi-physical field signals is realized. According to the diagnosis method, through wavelet denoising and multi-dimensional feature extraction, a feature vector of multi-state parameter fusion in the process from partial discharge to arcing is constructed, and accurate classification of fault types is realized by using a support vector machine (SVM) model. The diagnosis method has high accuracy and early warning capability, effectively overcomes the limitation of single parameter diagnosis, and provides reliable technical support for transformer fault research and intelligent operation and maintenance.

The invention discloses a transformer sound signal adaptive wavelet optimization and feature extraction method and system, and the method comprises the steps: calling a Pywt wavelet analysis library, decomposing an original signal according to a decomposition layer number J, and obtaining a multi-layer detail signal; for each layer of detail signals, the following steps are executed: introducing an M estimator to improve a noise variance calculation model, and calculating a standard deviation and a unified monitoring threshold value; constructing a dynamic threshold value based on a denoising signal approximation error minimization criterion; designing a correction factor; correcting the wavelet coefficient of each layer of detail signal; reconstructing a pure signal by using an inverse decomposition method; dividing the pure signal into a plurality of short-time signals; extracting an MFCC feature vector of each short-time signal; weighting and screening MFCC feature vectors by adopting a support vector machine recursive feature elimination method; and compressing the dimension of the feature vector in combination with a principal component analysis algorithm to generate a final feature matrix. According to the method, the problems of contradiction between noise suppression and signal fidelity and low recognition rate caused by high-dimensional feature redundancy in a traditional method are solved.

The invention relates to a rolling bearing fault diagnosis method and device based on a composite multi-scale attention entropy and an optimized SVM, and a medium, and the method comprises the steps: collecting vibration signals of a rolling bearing in different states, obtaining the damage size grade in each state according to the vibration signals, and obtaining a fault data set; performing coarse graining processing on the vibration signal to obtain a coarse grain sequence, and calculating an entropy sequence of the coarse grain sequence under different scale factors by adopting a composite multi-scale attention entropy algorithm improved by a fractional order algorithm; entropy values of a plurality of first scale factors in the entropy sequence are selected as bearing fault feature vectors; optimizing hyper-parameters of the support vector machine by using a collaborative group optimization algorithm; and constructing a support vector machine classification model based on the optimal hyper-parameter, and performing fault prediction and judgment on the test sample. Compared with the prior art, the method has the advantages of high noise robustness, high global convergence, high adaptability and the like.

The invention discloses a welding spot defect detection method and system, a storage medium and equipment, and belongs to the technical field of welding detection, and the method comprises the following steps: S1, obtaining welding spot data of a to-be-detected piece; s2, inputting the welding spot data into different preset convolutional neural network models, and respectively extracting shape features, color features and texture features of the welding spots; s3, fusing the shape features, the color features and the texture features of the welding spots by using a cross attention mechanism and a gated cross-modal attention mechanism; s4, inputting the fused feature information into a preset support vector machine model, dividing the welding spot data into a normal type and a defect type, and outputting a defect probability; and S5, inputting the welding spot data judged as the defect type into a preset defect classification model to judge the defect type of the welding spot. According to the method, deep coupling of shapes, colors and textures is realized through a gating cross-modal attention mechanism, the detection rate of coupled welding spot defects can be effectively improved, and the detection accuracy of welding spots is improved.

The invention relates to the technical field of medical health and artificial intelligence, in particular to an old people common disease occurrence and development risk prediction method based on integrated machine learning, which comprises the steps of constructing a standardized data set, screening key variables, training a base learner, combining prediction results, dynamically evaluating risks and the like. According to the method, multi-dimensional data features are integrated, a prediction model is constructed by using algorithms such as a random forest and a support vector machine, model parameters are optimized in combination with a verification set, and a high-precision co-disease risk prediction result is finally output. According to the invention, accurate assessment of the co-illness risk of the old people can be realized, and a scientific basis is provided for personalized health management.

The invention discloses a hail early warning method and system based on lightning jump and a support vector machine, and the method comprises the steps: obtaining a combined reflectivity factor, a vertical integral liquid water content and echo top height data through radar networking data, recognizing a severe convective echo region, and calculating a lightning time change rate and a standard deviation thereof; a potential hail event is screened in combination with a lightning jump increase condition, a vertical integral liquid water content maximum value VILmax, a jump increase GVIL and a density VILD are further extracted as feature parameters, and the feature parameters are input into a support vector machine classification model based on historical sample training for secondary judgment, so that the false alarm rate of a traditional 2 sigma algorithm is effectively reduced, and the hail early warning accuracy is improved.

The invention relates to the technical field of aluminum alloy surface detection, and discloses an aluminum alloy surface defect recognition method and system based on image recognition, and the method comprises the steps: carrying out the image collection of an aluminum alloy surface, and obtaining an original image of the aluminum alloy surface; preprocessing the collected original image to obtain a preprocessed image; performing feature extraction on the preprocessed image, and extracting geometric features, texture features and color features of defects; screening the extracted features by adopting a feature selection method based on the combination of a genetic algorithm and a support vector machine to obtain a feature subset; inputting the feature subset into a GNN-GAN model, outputting the type, position and size of a defect according to a defect identification result, and marking and recording the defect; according to the invention, the labor cost and the time cost required by manual detection are reduced, the product rework and the rejection rate caused by missing detection are reduced, and the quality stability of the aluminum alloy product is ensured.

The invention provides a laser cutting method for irregular automobile parts, which comprises the following steps: acquiring part surface data through laser radar scanning and ultrasonic measurement, constructing a three-dimensional geometric model and generating a defect distribution diagram; using an edge detection algorithm to identify transition section features, combining a genetic algorithm to optimize cutting parameters, and dynamically adjusting laser power, cutting speed and gas pressure; in the cutting process, a high-speed camera and an infrared sensor are adopted for real-time monitoring, and defect types are analyzed and recognized based on a convolutional neural network; according to the defect distribution condition, a support vector machine is used for predicting and generating correction parameters, and a cutting path is optimized through an A star algorithm; and finally, an OPC UA protocol is adopted to control execution of the laser cutting system, and quality evaluation is carried out. Accurate recognition, intelligent repair and quality control of surface defects of complex parts are achieved, and the precision and efficiency of laser cutting are improved.

The invention discloses a vulnerability detection method and system based on semantic sensitive contrast learning and graph representation, and belongs to the technical field of vulnerability detection.The method comprises the steps that source codes and transformed codes are input into a vulnerability detection model to be processed, and a vulnerability classification result is obtained; the vulnerability detection model comprises a semantic learning module, a graph representation learning module, a feature fusion module and a support vector machine; wherein the construction process of the vulnerability detection model comprises the following steps: taking a source code and a transformed code as a positive sample pair for comparative learning to obtain code semantic feature embedding; the source code is represented as a graph structure, the graph structure is processed, and then code structure feature embedding is obtained; and performing feature fusion on the code semantic feature embedding and the code structure feature embedding to obtain fusion vectors, and classifying the fusion vectors to obtain a vulnerability classification result. The model is guided to learn semantics related to vulnerabilities through comparative learning, and meanwhile, the accuracy of vulnerability detection is improved in combination with grammatical structure features of codes.

The invention discloses a magnetostriction liquid level meter temperature compensation method and system based on a support vector machine. The method comprises the steps that the currently captured original pulse number is obtained through a magnetostriction liquid level meter; the current environment temperature is detected in real time through a high-precision temperature sensor in the magnetostrictive liquid level meter; taking the original pulse number and the current environment temperature as input features, sending the input features to a pre-trained support vector machine regression model (SVM), and outputting a displacement error prediction value in the current temperature environment; and correcting the original measurement result according to the error prediction value to obtain a real pulse measurement value after temperature compensation. The system comprises a time-to-digital conversion chip, a temperature chip temperature measurement module, a support vector machine reasoning module, a pulse compensation module and a distance calculation module. According to the method, nonlinear modeling can be carried out on measurement errors caused by environment temperature changes, so that the liquid level measurement result is dynamically corrected, and the overall robustness and precision of a measurement system are improved.

The invention discloses a billet internal defect classification method, system and device based on PSO-SVM, and the method comprises the steps: S1, collecting billet internal ultrasonic signal data, and carrying out the preprocessing; s2, inputting the preprocessed data into a pre-trained defect classification model for identification; wherein the defect classification model comprises a particle swarm algorithm and a support vector machine; and S3, outputting a billet internal defect type identification result. The method is suitable for flaw detection of the continuous casting billet, is a more accurate internal flaw detection identification method, provides acceptable improvement measures for production of forgings, and is beneficial to quality control of the forgings.

The invention provides an algorithm plug-in collaborative development method and system based on multi-version control, and relates to the technical field of software development, and the method comprises the following steps: creating a development task and distributing the development task to a plurality of development nodes, constructing a dependency graph and analyzing version conflicts, performing code review, and deploying and testing in test environments of different version underlying frameworks. The compatibility score is calculated through the support vector machine, the release version is determined, and the release information is synchronized, so that collaborative development of multiple development nodes is realized, dependency conflicts are recognized in advance, and the compatibility of the algorithm plug-in under a multi-version framework is ensured.

The invention discloses a multi-channel output state real-time monitoring method and system based on voiceprint recognition, and the method comprises the following steps: synchronously collecting real-time sound signals outputted by each channel, and sequentially carrying out the enhancement, framing, feature extraction and feature fusion, thereby forming a multi-dimensional voiceprint feature vector of each channel; collecting voiceprint feature vectors of standard test sounds output by each sound channel, and training through a support vector machine to obtain a standard state feature space of each sound channel; obtaining a comprehensive abnormal index through the multi-dimensional voiceprint feature vector and the standard state feature space, and recognizing an abnormal type; and a regulation and control instruction is generated according to the abnormality type, and the abnormality type, the occurrence time and the sound channel position are transmitted to a monitoring terminal in real time through a bus communication unit. The method is used for solving the technical problems of insufficient synchronous acquisition precision, single feature extraction dimension, incomplete standard state space modeling, poor anomaly detection robustness and incapability of accurately classifying anomaly types in the real-time monitoring of the existing multichannel output system.