207 results about "Neural network classification" patented technology

The invention discloses an intelligent tea identification and grading method and system based on a neural network, and relates to the technical field of intelligent tea identification and grading, in-depth learning and feature extraction are performed on tea images through a neural network classification module and a convolutional neural network, and fine features such as color, shape and size of tea can be identified. And high-precision grading is carried out. Compared with traditional artificial grading, the neural network system eliminates interference of artificial deviation and subjective factors, and ensures consistency and reliability of grading results. Through adaptive filtering and image enhancement algorithms in the image preprocessing module, the system can automatically carry out illumination correction, noise removal, background removal, image sharpening and other processing in the image acquisition stage, and the definition and quality of the image are greatly improved. According to the system, through automatic image acquisition, processing, feature extraction and grading processes, manual intervention and operation can be greatly reduced, and the tea grading efficiency and speed are improved.

The invention discloses a battery state evaluation method based on machine learning, and the method comprises the steps: collecting battery data in a plurality of battery use environments, and marking a battery state; carrying out sample generation on the collected battery data by adopting an SMOTE algorithm based on low-rank matrix interpolation; forming an expanded data set by the generated data and the original data, training the data pair feature extraction model, and optimizing the training process of the feature extraction model by adopting a self-adaptive oscillation method in the training process; inputting the features corresponding to the battery state data extracted by the feature extraction model into a classifier model of a Riemann neural network classification algorithm based on fractional derivation to train the classifier model; and inputting the collected battery data of the to-be-evaluated battery into the trained feature extraction model for feature extraction, and inputting the extracted features into the trained classifier model for classification to obtain the state of the to-be-evaluated battery. According to the invention, the health state evaluation precision of the battery can be improved.

The invention discloses an error diagnosis classification and intelligent review planning method based on multi-dimensional analysis, which is suitable for a personalized learning support system. The method comprises six steps of error collection, automatic labeling, error attribution, capability evaluation, knowledge point correlation analysis and dynamic review planning, and integrates OCR recognition, TF-IDF feature extraction, convolutional neural network classification, knowledge graph modeling and Q-learning intelligent strategies. The system can realize accurate classification and reason diagnosis of wrong questions based on answering behaviors and cognitive features of students, optimizes a review plan in combination with a forgetting curve and habit error, supports an online and offline mixed review mode, and significantly improves learning efficiency and personalized adaptation ability.

The invention provides an adversarial digital camouflage generation method, device and equipment, and the method comprises the steps: carrying out the encoding of an obtained target background image based on an encoder, and forming a first hidden variable; performing denoising processing on the first hidden variable in a hidden space based on a denoising diffusion model to form a second hidden variable; processing the second hidden variable based on a decoder to generate a digital camouflage image; processing the first hidden variable based on an adversarial noise generator to generate an adversarial noise image; and obtaining an adversarial digital camouflage image corresponding to the target background image based on the combination of the camouflage image and the adversarial noise image. According to the adversarial digital camouflage generation method provided by the invention, the adversarial digital camouflage image can be quickly and accurately generated, so that the camouflage effect is good and the adversarial digital camouflage image is not easy to find when facing a neural network classification model.

The invention discloses an airborne radar identification method based on multi-scale signal decomposition and depth feature fusion, belongs to the technical field of radar target identification, is used for unmanned aerial vehicle radar target identification, and comprises the following steps: decomposing an original dynamic radar cross section signal into a plurality of intrinsic mode components by adopting an adaptive noise complete set empirical mode decomposition method; self-adaptive wavelet soft threshold denoising is carried out on all the components in a unified mode so as to improve the signal-to-noise ratio and suppress background interference; effective components are screened out by calculating correlation coefficients of all intrinsic mode components and original signals, a radar cross section signal sequence after noise reduction is reconstructed, and multilevel feature fusion is carried out; and inputting the feature vector into a stacked auto-encoder deep neural network classification model, and introducing a particle swarm optimization algorithm to optimize the classification model. According to the method, the signal-to-noise ratio of the signal and the recognition accuracy of the classification model in a low signal-to-noise ratio environment are remarkably improved, and high-precision recognition and classification of different types of radar cross section targets are realized.

The invention relates to a guiding type improved neural network ankle joint fracture classification method, system and device, and belongs to the field of computer-aided medical image processing.The classification method comprises the steps that S1, database images are collected and enhanced; s2, training a segmentation network model by using the segmentation data marked with the fibula region; s3, inputting the fracture type classification data into the trained segmentation network model, predicting a fibula area, and taking a prediction result as guidance to act on the improved neural network classification model; s4, inputting the fracture type classification data into the improved neural network classification model guided by the prediction result for training; and S5, verifying a classification result by using a test set, and evaluating the model by using a statistical index. The ankle joint fracture type can be judged, the method has the advantages of being high in diagnosis accuracy, high in speed and the like, and the problem that a traditional deep learning method is prone to being interfered by other information in images in the medical field to make misjudgment is effectively solved.

The invention discloses a wafer defect image classification method based on adaptive parameter selection OPTICS. The method comprises the following steps: performing isolation and twin outlier screening on wafer defect images; the designed intra-cluster density and inter-cluster density function considering the shape and size of the cluster is used as a comprehensive clustering index; a K-D tree algorithm is adopted to select the range of MinPts parameters of the OPTICS, and the optimal MinPts clustering parameters are obtained based on the comprehensive clustering indexes; a depth-first search algorithm based on a graph theory is adopted to analyze connectivity between data points to merge spatial adjacent clusters, and a clustering result is obtained; and using the improved neural network model to classify clustering results. According to the method, the OPTICS is improved from the three aspects of outlier removal of the wafer defect image, range selection of MinPts parameters of the OPTICS algorithm and self-adaptive parameter selection of the MinPts parameters of the OPTICS algorithm, and the preprocessed data set is classified by adopting the improved neural network classification model, so that the wafer defect classification precision is effectively improved.

The invention relates to the field of die forging press anomaly detection, in particular to a large die forging press anomaly detection method, which comprises the steps of collecting multi-dimensional time sequence training data of a die forging press, aligning timestamps and processing missing values; dividing the training data into a plurality of segments through a sliding window, and dividing the operation stages of the die forging press into five classes; performing tagging processing on the data fragments, calculating a Pearson's correlation coefficient matrix, and generating a corresponding correlation threshold matrix; training a neural network classification model based on the training data; collecting detection data of the die forging press in real time, and judging the current operation stage; selecting, calculating and comparing a correlation threshold matrix and a real-time correlation coefficient matrix according to the current operation stage; and if the real-time correlation coefficient is lower than the correlation threshold matrix coefficient, abnormal early warning is triggered. According to the method, anomaly detection is carried out on the sensor correlation in different stages, so that the accuracy of anomaly detection of a correlation algorithm is improved, and the false alarm rate and the missing report rate are reduced.

The present invention belongs to the technical field of mechanical fault diagnosis and signal processing, and specifically relates to a bearing fault diagnosis method, system, equipment and medium based on a multi-channel analog filter feature network. The method constructs a multi-channel analog filter feature extraction network, uses multiple parallel bandpass filter channels to extract bearing vibration signal characteristics, and combines an analog neural network classifier to perform fault classification. Signal preprocessing includes removing DC components, amplitude normalization and segmented processing; the filter passband parameters and classifier parameters are jointly optimized through a particle swarm algorithm, with classification accuracy and decision confidence as fitness functions. The system includes signal acquisition, preprocessing, multi-channel filtering, feature extraction, analog neural network classification and result display modules, achieving low-power, high-real-time bearing fault diagnosis. The present invention solves the problems of high power consumption and large delay of traditional digital processing methods, and is suitable for long-term monitoring and large-scale deployment in industrial sites.

The invention discloses a power transmission tower attitude monitoring and vibration intrusion identification method based on multi-source data fusion, and the method comprises the steps: carrying out the deep fusion of GPS displacement data and accelerometer inclination angle data, employing a dynamic weight fusion mechanism: adjusting a fusion coefficient alpha in real time according to the GPS signal quality, guaranteeing the accuracy and stability of data fusion, and at the same time, carrying out the real-time adjustment of the fusion coefficient alpha, and carrying out the recognition of the vibration intrusion of a power transmission tower. And continuously optimizing the error propagation model by taking the final inclination angle theta final of the power transmission tower as a state variable, updating the attitude estimation of the power transmission tower, and realizing + / -0.01-degree high-precision attitude monitoring. According to the invention, the system can output a high-precision three-dimensional attitude angle in real time by combining the rapid dynamic response of the accelerometer and the long-term stability of the GPS. Besides, an LCD-MPE (feature extraction technology) is introduced, lightweight neural network classification is combined, vibration signal sampling, noise reduction and abnormal signal segment identification, analysis and extraction are carried out on the transmission tower through an MEMS accelerometer, and second-level detection and behavior type discrimination of invasion behaviors around the transmission tower are realized. The core technical barrier from'inability perception 'to'accurate identification' is overcome, and subversive upgrading of power facility safety protection from'passive disposal 'to'active defense' is promoted.

The invention discloses a low-voltage power distribution network operation state detection method based on a fusion terminal, and relates to the technical field of power distribution networks, and the method comprises the steps that the fusion terminal obtains an original data set of a low-voltage power distribution network; performing data preprocessing on the original data set; performing feature extraction on the preprocessed data; performing feature fusion on the extracted features to obtain a fused feature vector, and dividing a training set and a test set; constructing a neural network classification model for training to obtain a state classification model, optimizing the state classification model, and inputting the fusion feature vector into the state classification model for classification; selecting abnormal state data for detection, and identifying an abnormal state type; and selecting normal state data for health assessment, and comprehensively evaluating the health state of the low-voltage distribution network through the health key indexes and the health assessment function. According to the invention, the monitoring precision of the low-voltage power distribution network is improved, the anomaly detection and health assessment capabilities are enhanced, and the resource configuration is optimized.

The invention relates to a quick connection method and system for a head-mounted Bluetooth headset and a smart television. The method comprises the following steps: carrying out parameter acquisition on connection between the head-mounted Bluetooth headset and the smart television to generate a standardized feature matrix; inputting the data into a parallel signal processor, respectively calculating a time domain evaluation parameter and a frequency domain evaluation parameter through a time domain processing unit and a frequency domain processing unit, and generating connection quality evaluation data after time-frequency entropy constraint fusion; performing neural network classification operation to generate a dynamic optimization parameter set; inputting the dynamic optimization parameter set into a rapid equipment scanning module, acquiring feature information of peripheral Bluetooth equipment by adopting a dual-stage scanning mechanism, and generating equipment feature association degree data; and calculating an optimal connection cost value, establishing Bluetooth connection when the optimal connection cost value meets a preset threshold condition, and generating a real-time performance evaluation result. According to the invention, the reliability and stability of the connection process are ensured, and the recognition efficiency and accuracy of the target equipment are remarkably improved.

The invention provides a multi-modal heterogeneous sensor energy efficiency optimization method and device, equipment and a storage medium, and the method comprises the steps: carrying out the lightweight convolutional neural network classification of user motion data collected by an acceleration sensor, and obtaining a scene feature vector; constructing a parameter incidence matrix according to the scene feature vector and the pre-determined parameters of the multiple monitoring sensors; performing resource allocation according to the parameter incidence matrix to obtain a parameter configuration table; and performing three-level scheduling according to the scene feature vector, the parameter configuration table and a preset working mode to obtain a target working mode of the plurality of monitoring sensors, and controlling the plurality of monitoring sensors to work according to the target working mode.

The invention discloses an improved satellite navigation deception detection system based on deep learning fusion multi-level time-frequency analysis and a detection method thereof. The system comprises a time-frequency transformation module, a time-frequency image processing module and a neural network classification module. The time-frequency transformation module comprises time-frequency transformation modes of short-time Fourier transformation, continuous wavelet transformation and Margenauu-Hill transformation, the time-frequency transformation module directly takes an intermediate-frequency digital signal as input, and the defect that the input signals are different due to different front ends is overcome; the time-frequency image processing module is composed of a time-frequency standardization module, a gray processing module and a multi-channel image fusion module. The multi-channel image fusion module fuses three types of time-frequency transformation characteristics, different time-frequency transformation methods show different performance expressions under different signal characteristics and environment conditions, and a multi-level time-frequency analysis method is fused to endow the system with higher signal intelligent adaptability, so that the system is excellent in performance when facing various signal types and complex scenes, and the system is more suitable for various signal types and complex scenes. And the anti-noise performance is improved.

The invention belongs to the technical field of mechanical fault diagnosis and signal processing, and particularly relates to a bearing fault diagnosis method, system and equipment based on a multi-channel analog filtering feature network and a medium. The method comprises the steps that a multi-channel analog filtering feature extraction network is constructed, and bearing vibration signal features are extracted through multiple band-pass filter channels arranged in parallel; and fault classification is carried out in combination with a simulated neural network classifier. The signal preprocessing comprises direct current component removal, amplitude normalization and segmentation processing; filter passband parameters and classifier parameters are jointly optimized through a particle swarm algorithm, and classification accuracy and decision confidence are used as fitness functions. The system comprises a signal acquisition module, a preprocessing module, a multi-channel filtering module, a feature extraction module, a simulation neural network classification module and a result display module, and realizes bearing fault diagnosis with low power consumption and high real-time performance. The method solves the problems that a traditional digital processing method is high in power consumption and large in delay, and is suitable for long-term monitoring and large-scale deployment of an industrial site.

The invention discloses a desert-area-oriented hybrid energy storage and wind-light prediction collaborative optimization method, which comprises the following steps of: firstly, carrying out equipment modeling constraint on a multi-energy complementary system including a wind power generation system, a photovoltaic power generation system, a hybrid energy storage system and the like; establishing a wind and light prediction model by adopting BP neural network classification prediction optimized by a particle swarm algorithm; and performing joint optimization on the wind energy and illumination prediction model and the multi-energy complementary system optimization model containing the hybrid energy storage by using a differentiation creative search algorithm, and taking the wind energy and illumination prediction result as the input of the multi-energy complementary system optimization model containing the hybrid energy storage to obtain an optimal energy supply and energy storage scheduling scheme. The method has the advantages that the carbon emission is reduced, the consumption of new energy is promoted, the operation cost of the comprehensive energy system is reduced, the energy supply pressure is reduced, and the like.

The invention relates to the technical field of education information, in particular to a student group classification prediction method, device, system, equipment and medium, and the method comprises the steps: collecting the multi-dimensional data of each student through an online and offline combined mode, and obtaining a multi-dimensional data set; clustering the multi-dimensional data set by adopting a clustering algorithm to obtain a classification result of the student group; and constructing a neural network classification prediction model based on the multi-dimensional data set and the classification result, training the neural network classification prediction model, and inputting the multi-dimensional data of the to-be-tested student into the trained neural network classification prediction model for analysis to obtain the student group category of the student. Therefore, the diversity and comprehensiveness of data sources are ensured through online and offline multi-channel data collection, the accuracy of data analysis is improved by combining a clustering algorithm and a neural network classification prediction model, detailed classification of student groups is realized, the potential value of students is rapidly evaluated, and the students' experience is improved. And the purpose of helping enterprises and educational institutions to carry out strategic planning is achieved.

The invention discloses an arrhythmia detection method based on a pulse neural network. The method comprises the following steps: performing incremental modulation pulse coding and pooling operation on a target electrocardiosignal to obtain a corresponding pulse sequence; and inputting the pulse sequence into a trained multi-level pulse neural network classification model to obtain an arrhythmia detection result. Wherein the multi-level pulse neural network classification model comprises a weight sharing layer and a plurality of cascaded classifiers, the weight sharing layer is used for extracting electrocardiosignal feature information from the pulse sequence, and the plurality of classifiers are used for obtaining an arrhythmia detection result based on the electrocardiosignal feature information. According to the method, on the premise that the accuracy is equivalent, the classification result is more refined, and the requirements for hardware storage resources and computing resources are reduced.

The invention relates to the technical field of network security, in particular to an attack intention behavior recognition method and device, and the method comprises the steps: obtaining a plurality of attack features of a plurality of historical attack gangs, and marking a preset state for each attack feature, so as to obtain a plurality of marked attack features; and training a pre-constructed deep neural network classification model by using the plurality of labeled attack features to obtain a gang attack intention-based recognition model, and recognizing the actual state of the target attack gang by using the gang attack intention-based recognition model. Therefore, the problems that most of existing security products are lack of deep analysis capability, so that comprehensive understanding of behaviors of attackers is insufficient, the real intentions of the attackers are difficult to accurately judge, response can only be performed based on the attacking behaviors, potential threats cannot be actively predicted and recognized and the like are solved.

The invention discloses a method for identifying an epimedium koreanum planting area based on multi-source remote sensing image fusion in the technical field of under-forest planting, and the method comprises the steps: obtaining multi-source remote sensing data of a target area, carrying out the preprocessing, and carrying out the recognition of an epimedium koreanum planting area based on the preprocessed multi-source remote sensing data; extracting multi-source feature data related to under-forest planting of the Korean epimedium, fusing the extracted multi-source features, inputting the fused multi-source features into a deep neural network classification model of a cross-modal attention mechanism, processing the fused multi-source features, and obtaining a deep neural network classification model of the cross-modal attention mechanism; according to the method, multi-source remote sensing data advantages of optics, radars, laser radars and the like are integrated, the limitation of a single data source in under-forest environment monitoring is overcome, and the method has the advantages of being high in accuracy, high in accuracy and high in reliability. And aiming at special growth requirements of the epimedium koreanum, key parameters such as canopy density and gradient are quantified, and accurate evaluation of ecological suitability is realized.

The invention relates to a portable photovoltaic energy storage system fault arc fire multi-mode early warning device. According to the device, a portable automatic fault arc simulation generation device is built through a Raspberry Pi, a stepping motor, an arc generator and a mobile power supply; a photovoltaic energy storage system fault arc multi-mode information acquisition system is established based on a Raspberry Pi, a camera module, a smog sensor, a flame sensor, a temperature sensor, a current sensor, a voltage sensor and the like, and a DC fault arc fire multi-mode information database is established. The photovoltaic energy storage system fault arc multi-mode early warning method is designed to solve the problems that information is incomplete and context is missing due to the fact that single-mode text data is combined with a machine learning classification algorithm and image data is combined with a deep neural network classification algorithm. The multi-modal fusion has the advantage of information complementation, and especially has stronger anti-interference capability for multi-modal early warning in a monitoring environment with strong electromagnetic interference and frequent illumination sudden change interference.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a computer network intrusion detection method and system based on artificial intelligence, and the method comprises the steps: constructing a finite state automaton to carry out the state jump compliance verification of a connection flow, intercepting the illegal flow, only sending the compliance flow into a feature engineering module, and extracting the multi-dimensional behavior features; inputting a deep neural network classification model to determine whether the behavior is an intrusion behavior; according to the technical scheme provided by the invention, the data input to the artificial intelligence model can be ensured to have complete compliance on the protocol level, so that the model is prevented from learning false feature association caused by protocol violation, and the antagonistic attack based on protocol state jump can be effectively resisted on the premise of not depending on adversarial training.

The application relates to the technical field of positioning device signal processing and positioning precision, and particularly discloses a non-line-of-sight classification and error compensation method and system based on a double convolution neural network, the method comprising the following steps: acquiring channel impulse response data of an ultra-wideband signal, and pre-processing the channel impulse response data to obtain an amplitude sequence in real number form; constructing a double convolution neural network classification model, the double convolution neural network classification model comprising parallel large-kernel convolution blocks and small-kernel convolution blocks, which are respectively used for extracting macroscopic waveform features and microscopic fluctuation features from the amplitude sequence; and performing classification training after fusing the macroscopic waveform features and the microscopic fluctuation features; and based on the trained classification model, the regression compensation of ranging errors of non-line-of-sight signals is performed, macroscopic waveform contour features and microscopic local fluctuation features can be simultaneously extracted from data, and a complementary feature extraction mechanism can more comprehensively describe the differences between LOS / NLOS signals compared with a single-structure network.

The present application relates to a kind of generalization adversarial sample group generation method based on easily confused class feature injection, belong to the technical field of adversarial attack of computer vision field.It introduces the class disturbance update momentum in the direction of easily confused class feature vector in the process of generating class-oriented generalization disturbance sample group, the class disturbance update momentum makes the adjacent easily confused class of adversarial example faster update, to realize the generalization attack to class.The present application only integrates the features of easily confused class in the same class image, forms disturbance template, realizes class-oriented generalization disturbance sample group;Variable step size iterative attack means is used to explore easily confused class in the process of neural network classification, realize easily confused feature injection, while introducing class momentum parameter in the process of iterative attack, improve attack efficiency and alleviate the over disturbance shortcomings of generalization disturbance.

The application discloses a dynamic convolution deep neural network classification method with global and local attention, and belongs to the technical field of image classification and artificial intelligence, wherein a dynamic convolution kernel module with global and local attention is designed and built to improve the traditional convolution operation by analyzing the convolution layer in the deep convolution neural network. The method comprises the following specific steps: S1, dividing a data set into a training set and a test set; S2, designing and building a dynamic convolution kernel module with global and local attention; and S3, training and testing the built dynamic convolution deep neural network with global and local attention on a natural image classification data set, so as to reduce the parameter quantity and the calculation complexity of the network, thereby better solving the image classification problem.

The present invention discloses a method for detecting the operating state of a low-voltage distribution network based on a fusion terminal, which relates to the technical field of distribution networks. The method includes: the fusion terminal acquires the original data set of the low-voltage distribution network; performs data preprocessing on the original data set; extracts features from the preprocessed data; performs feature fusion on the extracted features to obtain a fused feature vector, and divides the training set and the test set; constructs a neural network classification model for training to obtain a state classification model, and optimizes the state classification model, inputs the fused feature vector into the state classification model for classification; selects abnormal state data for detection to identify the types of abnormal states; selects normal state data for health assessment, and comprehensively evaluates the health state of the low-voltage distribution network through health key indicators and a health assessment function. The present invention improves the monitoring accuracy of the low-voltage distribution network, enhances the abnormal detection and health assessment capabilities, and optimizes the resource allocation.

The application belongs to the technical field of image processing, and particularly relates to a defect classification method for automobile injection molding parts based on database comparison, which comprises the following steps: extracting a feature vector fused with multi-scale artificial information and deep learning information for normal, defect and to-be-detected areas; realizing efficient preliminary screening of abnormal areas by calculating a local anomaly factor of the to-be-detected area and comparing the local anomaly factor with a dynamic determination threshold generated based on defect data; and splicing the original feature vector and the quantized local anomaly factor into an enhanced feature vector, inputting the enhanced feature vector into a neural network classification model, and performing accurate defect category determination. The two-stage strategy of dynamic threshold preliminary screening and enhanced feature accurate division improves the accuracy and stability of defect detection of automobile injection molding parts.

The invention relates to the technical field of block chain power supply management, and discloses a distributed power supply transaction management method of block chain nodes. According to the method, historical normal power supply transaction data is collected to establish a reference database, and the transaction detection period duration is determined according to the data size of a real-time power supply transaction data set. And monitoring node transaction records in a detection period, calculating a transaction frequency average value as a detection threshold value, and identifying suspected abnormal transactions in combination with the reference database. After the suspected abnormal transaction is confirmed, extracting the total transaction amount and the time distribution characteristic to judge whether the transaction is the abnormal transaction, and counting the occurrence frequency to calculate an initial risk coefficient; and the category of the abnormal transaction, the participation node identifier and the behavior pattern are extracted and converted into feature vectors, and malicious transaction node groups are grouped, analyzed and detected through a neural network classification algorithm. And if the malicious group exists, calculating an adjustment coefficient to correct the initial risk coefficient, and dynamically adjusting parameters of the next detection period.

The present application provides a blood cell classification method, in which the impedance pulse signal of the blood sample to be classified is input into a trained target neural network classification model. The target neural network classification model is trained by a large number of impedance pulse signal samples with classification result label information. It has a strong pulse morphology feature extraction capability, so that the classification result of the impedance pulse signal can be determined based on the morphological characteristics of the impedance pulse signal of the blood sample to be classified. Compared with the existing technology, the classification result is more accurate, and it can identify the impedance pulse signal caused by interference factors, and can perform identification and classification based on the impedance pulse signal, with higher real-time performance. In addition, the present application also provides a blood cell analyzer and a storage medium.

The invention discloses a buried pipeline leakage signal collaborative classification system based on a twin neural network. The system comprises a data acquisition module, a signal preprocessing module, a twin neural network classification module, an intelligent decision module and a man-machine interaction module. The twin neural network comprises two sub-networks sharing weights, and the two sub-networks respectively process multi-modal fusion signals of the same pipe section at different moments, so that intelligent detection of pipeline state changes is realized. According to the method, the leakage detection precision can be effectively improved, the false alarm rate is reduced, accurate classification of different leakage types is realized, and reliable guarantee is provided for safe operation of buried pipelines. According to the system, the DTS is matched with the DAS technology, and intelligent identification and classification of pipeline leakage signals are realized by utilizing a laying mode that an optical cable is attached to a buried pipeline in an accompanying manner.