179 results about "Support vector machine classifier" patented technology

The invention discloses a railway track damage detection method, and belongs to the technical field of railway track detection. According to the method, an image acquisition module and an ultrasonic detection module are installed at the bottom of a track detection vehicle, the detection vehicle is controlled to run, and track top face and side face image sequences and ultrasonic reflection signals are acquired; preprocessing the image, and respectively inputting the image into a deep convolutional neural network model and a support vector machine classifier to obtain a crack identification result and a wear level; processing an ultrasonic reflection signal, and judging a layering defect; and finally fusing the data, marking a damage position and generating a structured detection report. According to the method, the problems of incomplete detection, low precision and the like in the existing railway track damage detection are solved, efficient detection of track surface cracks, side abrasion and internal layering defects is realized through collaborative acquisition of the multi-modal sensor, intelligent algorithm processing and data fusion, and the comprehensiveness and reliability of detection are improved.

The invention relates to the technical field of big data, and discloses a space-time big data abnormal event detection method and device and a storage medium. The method comprises the following steps: performing timestamp unification and space coordinate standardization processing on urban space-time big data to obtain a standardized space-time data set; feature vector cosine similarity between each data point in the standardized spatio-temporal data set and other data points is calculated to obtain a target similarity value, and an initial neighborhood data set is selected; performing space-time density distribution calculation on the initial neighborhood data set to obtain a core neighborhood data set; performing time sequence feature and spatial distribution feature extraction on the core neighborhood data set to obtain a fusion feature weight matrix; and inputting the fusion feature weight matrix into a support vector machine classifier to perform abnormal event recognition to obtain an abnormal event detection result, thereby improving the classification capability of high-dimensional nonlinear features, and simultaneously realizing all-directional feature description and detection of abnormal events.

The invention discloses an efficient cross-season energy storage energy pile, and relates to the technical field of new energy and energy conservation. The problems that in an existing energy storage system, the thermal load prediction error is large, underground thermal diffusion attenuation is caused, the heat exchange capacity is lowered, and the geological adaptability is insufficient are solved. According to the scheme, an LA mixed time sequence prediction model is adopted to optimize load prediction, a distributed optical fiber temperature measurement array and a finite element inversion algorithm are combined to accurately reconstruct a stratum temperature field, and the geological type is identified based on a support vector machine classifier to realize self-adaptive regulation and control of heat exchange parameters; meanwhile, the heat pump power, the circulating pump frequency and the heat charging and discharging rate of the phase change material are optimized through a depth deterministic strategy gradient algorithm and a gradient heat release strategy; the heat exchange efficiency, the long-term stability and the complex environment adaptability of the cross-season energy storage system are remarkably improved, and the energy-saving effect of a building energy supply system is improved.

The invention relates to an epileptic seizure detection method and system based on self-attention mechanism and GRU-LSTM fusion, and the method comprises the following steps: (1) carrying out the preprocessing of original electroencephalogram signal data, and extracting time domain and nonlinear features; (2) randomly dividing the data set into a training set, a test set and a verification set; (3) constructing a deep learning model fusing a self-attention mechanism, a gating circulation unit and a long short-term memory network; (4) extracting fusion features from the trained deep learning model, and inputting the fusion features into a support vector machine classifier to perform epileptic seizure and non-seizure classification; and (5) outputting a classification result through multi-modal feature fusion, long and short term dependence modeling and adaptive feature selection. The method has the advantages that a self-attention mechanism, a gating circulation unit, a long-short-term memory network and a support vector machine classifier are combined, deep features of electroencephalogram signals are extracted through a multi-stage processing flow, and finally epileptic seizure and non-seizure classification is carried out.

The invention belongs to the technical field of radar signal processing and target identification, and particularly relates to a maritime hybrid target identification method and system based on modal decomposition and reconstruction, and the method comprises the steps: carrying out the variational modal decomposition of a radar echo signal of a maritime hybrid target, and decomposing an original signal into a plurality of intrinsic modal signals; estimating a background noise energy reference and removing noise modals based on the decomposed modal signals, clustering the remaining modals according to the center frequency, and combining the modals with the frequency within the center frequency range into an independent single-target signal which is of the same target and is reconstructed into an independent single-target signal; respectively carrying out time-frequency analysis on each reconstructed single target signal to obtain a corresponding time-frequency diagram, and extracting time-frequency domain features from the time-frequency diagram; and inputting the extracted features into a trained support vector machine classifier to realize automatic identification of the ship target and the floating target. A ship target and a floating target can be distinguished more accurately in a mixture scene, and the stability of tracking identification is improved.

The invention discloses a CT image segmentation method for spine surgery, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining a spine CT image of a patient, and carrying out the preprocessing of the spine CT image; adopting a centrum gap detection method to detect a centrum gap region of a spine in the preprocessed CT image, and performing automatic segmentation on each centrum based on centrum gap region information to obtain a preliminary segmentation result of the centrum; extracting local texture features of the intervertebral disc and the nerve root in the intervertebral space area, and classifying the local texture features through a classifier to generate a preliminary segmentation result of the intervertebral disc and the nerve root; performing rigid registration and non-rigid registration on the initial segmentation results of the vertebral body, the intervertebral disc and the nerve root and the standard three-dimensional model, and identifying an abnormal region in segmentation; by extracting local texture features of the intervertebral disc and the nerve root and combining a support vector machine classifier, accurate segmentation of various structures can be realized.

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 discloses a sand and dust vertical flux high-precision inversion method based on a laser radar. The method comprises the following steps: acquiring multi-wavelength back scattering and polarization information by using a 355 nm, 532 nm and 1064 nm three-wavelength polarization laser radar system; constructing a five-dimensional optical feature vector space, and combining a support vector machine classifier to realize automatic identification of dust particles; a variational data assimilation technology is adopted to fuse radar observation and numerical forecasting information to invert a three-dimensional wind field; inverting sand and dust mass concentration vertical distribution based on the corrected particle spectrum distribution model; flux calculation and uncertainty quantization are realized through adaptive weighted fusion and a Monte Carlo method; the method has the characteristics of high temporal-spatial resolution, high precision, strong adaptability and the like, and can be widely applied to the fields of weather forecast, environment monitoring, climate research and the like.

The invention provides a multi-scale feature fused textile quality detection method, which comprises the following steps of: aiming at multi-scale feature representation, obtaining a torsion angle and a torsion direction feature descriptor which are kept unchanged under image rotation transformation by calculating an invariant moment of a feature vector, and generating a multi-scale torsion feature vector; calculating a correlation coefficient between the torsion frequency and the torsion density in the multi-scale torsion feature vector and the brightness gradient and the brightness change rate in the brightness feature vector, and determining the correlation degree of the torsion feature and the brightness feature; according to the comprehensive feature vector, a support vector machine classifier is adopted to classify the textile image, and whether a defect area with an abnormal torsion area or an abnormal brightness dynamic range exists in the textile is judged; and in the defect area, local features of a torsion angle, a torsion amplitude, average brightness and brightness contrast are extracted, the torsion degree and the brightness abnormal degree of the defect area are judged through threshold comparison, and quantitative description of the defect is obtained.

The invention discloses a TCN-SVM rolling bearing fault diagnosis method fusing an SE attention mechanism, and belongs to the technical field of mechanical fault intelligent diagnosis. Aiming at the problems of feature redundancy, noise sensitivity, insufficient Softmax classifier generalization and the like existing in a traditional time sequence convolutional network (TCN), the invention provides a solution for collaborative optimization of a deep network and a support vector machine. The method comprises the following steps: acquiring a vibration signal of the rolling bearing; constructing a multi-fault sample set, and processing an original signal; constructing an SE-TCN feature extraction network, capturing multi-scale time sequence features by adopting expansion causal convolution, and embedding an SE module into a residual module to realize channel adaptive weighting; and a support vector machine (SVM) classifier decision function is constructed, and fault classification is completed through the RBF kernel SVM. Experiments show that the method has high fault recognition accuracy and robustness, and the problem of confusion of composite fault features of the rolling bearing is effectively solved.

The invention relates to a power distribution cabinet switch intelligent identification system and method, and the system comprises a data collection unit, a data processing unit, a feature extraction unit, a state classification unit, a test optimization unit, a multi-modal data fusion and analysis unit, and a system control unit. Generating a sub-image containing complete features in combination with a cutting extension algorithm; the feature extraction unit adopts a multi-scale convolution module and a Vision Transform to extract local and global features, and optimizes feature expression through an SE attention mechanism; the state classification unit is combined with a support vector machine (SVM) classifier and Focal Loss to accurately classify normal, closed and tripping states; the test optimization unit is used for enhancing the generalization ability of the model by utilizing Test Time Training (TTT) and self-supervised learning; and the multi-modal data fusion unit is used for modeling an interaction relationship between the image and the sensor data through a cross-modal Transform.

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 relates to the field of printed matter quality detection, in particular to a printed matter quality detection method based on artificial intelligence. Comprising the steps of collecting image data, constructing and enhancing a data set, correcting an image by adopting a feature point detection and matching algorithm, extracting an image feature vector through a pre-trained deep learning model VGG16, inputting the feature vector into a support vector machine classifier for quality classification, and giving a quality problem description detection report for unqualified printed matters. Wherein the image enhancement comprises zooming, contrast adjustment and color conversion; the feature point detection and matching algorithm can eliminate image distortion; the VGG16 model calculates feature mapping and extracts feature vectors through a convolution layer and a pooling layer, and finally standardization processing is carried out. And the support vector machine classifier outputs a classification result according to the decision function and performs quality feedback. The method improves the quality detection speed and efficiency of the printed matter.

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 discloses a non-intrusive dual-channel smart home security monitoring method, and belongs to the technical field of network security. Aiming at the problem that the existing smart home privacy security monitoring method has defects, an audio channel takes a pre-training model Vgish as an audio feature extractor, and a pixel spectrogram corresponding to each section of 960ms frame length audio obtained through discrete Fourier framing processing is vectorized into 128-dimensional feature representation; then, a support vector machine (SVM-RBF) classifier identifies and obtains an audio event category sequence Ea; five-tuple information contained in event traffic is extracted in a traffic channel to be matched with a predefined event-rule base mode, a traffic event category sequence Et is recognized, and then event alignment, verification and fusion are carried out on the sequences Es and Et so as to monitor the security problem in the smart home. According to the method, the accuracy of event identification of the two-channel model is proved through an experiment carried out on a self-built experiment platform, and meanwhile, the necessity of safety monitoring by adopting two-channel fusion is also proved.

The invention relates to the technical field of machine vision, in particular to an automatic recognition processing system and method for a multi-angle license plate image, electronic equipment and a storage medium, and the method comprises the steps: obtaining the multi-angle license plate image, carrying out the binarization processing of the image through employing a self-adaptive threshold segmentation algorithm, and extracting the contour of a license plate; correcting the license plate contour images at different angles by using perspective transformation; extracting local binary pattern features and HSV color histogram features of the corrected license plate contour image, performing character region cutting on the license plate by using a support vector machine (SVM) classifier, correcting a cutting result according to a license plate character arrangement rule to obtain an accurate character cutting image, constructing a multi-layer convolutional neural network, and obtaining a final license plate contour image. And inputting the cut character image into a network for identification and classification, and mapping and outputting license plate number information. According to the method, image processing, machine learning and deep learning technologies are comprehensively utilized, the accuracy and robustness of license plate recognition are comprehensively improved, and the method has a wide application prospect.

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 wood block surface flaw detection method based on image features, and belongs to the technical field of image data processing, and the method comprises the steps: obtaining an image of the surface of a target object, and carrying out the denoising and illumination normalization processing of the image, and obtaining a first image; performing wavelet transform decomposition to obtain edge distribution description data and shape description data corresponding to the first image, and generating geometric characteristic description; analyzing texture distribution of the first image in combination with a local binary pattern algorithm according to the geometric feature description, and generating a texture feature set; fusing the geometric feature description and the texture feature set to construct a multi-dimensional feature vector, and performing dimensionality reduction on the multi-dimensional feature vector by adopting principal component analysis to obtain a dimensionality-reduced feature vector; and inputting the dimension reduction feature vector into a support vector machine classifier, and outputting a defect classification result. The wood block surface flaw detection method based on the image features solves the problem that when an existing detection system faces complex flaw types, high-precision classification is difficult to achieve.

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 production line automation defect identification method and system based on eddy current detection. The system comprises an eddy current detection probe, a motion control module, a signal processing module and the like, an alternating magnetic field is applied to a metal workpiece through a double-frequency excitation coil, the distance between the probes is dynamically controlled through a laser positioning sensor, and after signal preprocessing and defect feature extraction, a support vector machine classifier decides defect types and triggers an execution mechanism to act. According to the method, time division multiplexing dual-frequency excitation is adopted, photoelectric cooperative detection is combined, and surface topography and eddy current data are fused. The system is connected with a factory MES (Manufacturing Execution System) through a Modbus-TCP (Transmission Control Protocol) so as to realize quality tracing. According to the scheme, the problems of single-frequency detection depth limitation, high vibration false detection rate, low manual sorting efficiency and data islanding are solved.

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 provides a building material detection and acceptance method and system, and the method comprises the steps: receiving related data of a to-be-detected building material; based on the data and a preset multi-dimensional acceptance standard, a virtual environment is constructed, parameters of the virtual environment are optimized, and it is ensured that simulation conditions are close to an actual use scene; in the optimized virtual environment, stress distribution of the material is calculated through finite element analysis, the degradation rate is predicted in combination with a machine learning algorithm, and a degradation rate predicted value is obtained; according to the predicted value, relevant data are adjusted through a multi-scale modeling technology and a random event triggering mechanism, and a response data set is determined; a support vector machine classifier is used for evaluating whether the response data set meets the acceptance standard or not, and an improvement suggestion report is generated for the non-conformity condition; and comprehensively improving the suggestion report, the response data set and the evaluation result, and generating a non-tampering detection report by means of a block chain technology. According to the invention, efficient and accurate detection and quality control of the building material are realized.

The invention discloses a tree species identification method based on visual word bag representation and state space modeling, and relates to the technical field of tree species cross section microscopic image identification, and the method is characterized in that the method comprises the following steps: S1, data set construction; s2, extracting local features by using a visual word bag model; s3, extracting global features by using the state space model; s4, performing multi-level feature fusion; s5, data oversampling and classifier training are carried out; and S6, identifying tree species. The technical problem to be solved by the invention is to provide a tree species identification method based on visual word bag representation and state space modeling, a state space model is introduced to model a feature sequence of a tree species image, a long-term dependency relationship of the feature sequence is mined, and global feature information is extracted. Through fusion of local and global features, multi-level feature representation with strong discrimination capability is constructed. Samples are balanced by adopting a synthetic minority class oversampling technology so as to improve the discrimination capability of a support vector machine classifier.

The invention discloses a circuit breaker fault diagnosis method based on multi-modal data fusion and few-sample learning. The method comprises the following steps: 1) acquiring different modal data of a circuit breaker at the same time by using a data acquisition unit; 2) preprocessing the acquired different modal data by using a data preprocessing unit to obtain primary characteristics of each group of modal data; 3) fusing the primary features of each group of modal data by using a data fusion unit to obtain fused features; and 4) classifying the fusion features by using a fault diagnosis unit to realize fault diagnosis. According to the method, the multi-source sensor information is fused, and the support vector machine classifier is trained by using the MAML-based meta-learning algorithm, so that the fault diagnosis of the circuit breaker under a small number of samples is realized. The circuit breaker fault diagnosis method effectively solves the problems that an existing circuit breaker fault diagnosis method depends on a large amount of labeled data and is difficult to adapt to new fault types.

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.