165 results about "Support vector machine classification" patented technology

The invention relates to the technical field of agricultural intelligent detection, in particular to a fruit tree disease and pest detection method based on machine vision, which comprises the following steps: acquiring visible light, infrared light and ultraviolet light images of a fruit tree at multiple angles through a multispectral camera to obtain complete fruit tree surface information, and preprocessing the acquired images to obtain a fruit tree disease and pest detection result; comprising the steps of denoising, illumination correction and image alignment, extracting feature vectors, including the color, shape and size of scabs, pest distribution and other features, of pest and disease damage from preprocessed images through a convolutional neural network, fusing the pest and disease damage features with environmental data by using a multi-modal data fusion algorithm, and enhancing the accuracy of pest and disease damage classification. Classifying and identifying diseases and pests based on a support vector machine classification algorithm; according to the dynamically adjusted threshold value and suggestions provided by the expert system, a disease and pest diagnosis report is generated, and prevention and control measure suggestions are provided; according to the invention, efficient and accurate detection and management of fruit tree diseases and insect pests can be realized, and the intelligent level of agricultural management is improved.

The invention discloses a data processing method and system based on image acquisition equipment, and relates to the technical field of computer vision, and the method comprises the steps: constructing CNN-VAE to generate a low-light image of a multi-modal image, employing an inverse attention module and a GAN model of a double-discriminator structure, and combining parallel expansion and expansion convolution to expand a training data set; images in the training data set are enhanced through double-branch U-Net, image details are enhanced through local variance adaptive Gaussian filtering, pixel gradients are classified through a support vector machine, and edge detection is carried out; resNet50 is adopted to extract features, and a candidate frame is accurately positioned in combination with RPN and non-maximum suppression. Through CNN-VAE and an anti-attention GAN model, training data is expanded, double-branch U-Net and local variance adaptive Gaussian filtering are utilized to enhance image details, edge detection is optimized through a support vector machine, ResNet50 feature extraction is adopted, an RPN network and non-maximum suppression are combined to position an object candidate frame, and high-precision object detection and recognition are realized. And the precision and robustness of image processing are obviously improved.

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 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 unmanned aerial vehicle detection, and discloses a common-aperture unmanned aerial vehicle detection method and system, and the method comprises the steps: obtaining multi-sensor original data and a state data set, and carrying out the standardization; in combination with the state data set, performing state updating and de-noising processing; if data missing or abnormal fluctuation is detected, filling and removing and time sequence storage are carried out, and a historical track sequence is formed; performing feature calculation to obtain a motion feature vector; classifying by using a support vector machine to obtain a target motion mode classification label; if the trajectory is an evasive type or an aggressive type, triggering a high-priority tracking process, and training by using a long-short-term memory network to obtain a predicted trajectory coordinate point set; unifying the coordinates to a global reference framework to obtain a global prediction trajectory, and calculating an intersection point of the global prediction trajectory and a preset no-fly zone to obtain an intersection detection result; generating a real-time alarm signal; and performing comprehensive threat assessment to obtain a threat target detection conclusion. According to the method, the threat assessment accuracy can be improved.

The invention discloses a method and a system for correcting wrong words in minority language text image recognition, and the method comprises the steps: obtaining a minority language text image, removing the high-frequency noise of the minority language text image through employing a preset adaptive threshold denoising algorithm based on wavelet transformation, and generating a denoised image. The morphological contour features of at least one character are extracted from the denoised image, the morphological contour features comprise stroke thickness, stroke number and stroke order direction, the features are input into a support vector machine classification model, the language category of the denoised image is determined, and a language classification result is generated. And loading a corresponding character segmentation model and a corpus according to a language classification result, carrying out character-by-character segmentation and pattern matching identification on the denoised image, and generating a preliminary identification result and an identification confidence score of each character. According to the method for correcting the wrong words in the text image recognition of the minority language, the accuracy of character recognition of the minority language and the capability of correcting the wrong words are improved, and recognition challenges in complex scenes can be coped with.

The invention provides an AI-based trachinotus ovatus juvenile fish culture water parasite prediction and prevention method and system, and the method comprises the steps: collecting multi-source data from a culture water body through a sensor array, including a nucleic acid signal, a fish school behavior track and an environment parameter sequence, and obtaining an original data set; unifying the data format and the acquisition frequency according to the original data set by adopting a time sequence alignment method, and obtaining an aligned feature flow; aiming at the aligned feature flow, applying a convolutional neural network to extract a space-time pattern of a nucleic acid signal and an abnormal index of a fish school behavior, and determining an extracted feature set; if the abnormal index in the extracted feature set exceeds a preset threshold value, a causal association between environmental parameters and parasitic risks is classified through a support vector machine, and a preliminary risk level is judged; simulating a water body change scene according to the comprehensive causal model to obtain a predicted trajectory of parasite propagation; and determining a quantitative score of the parasite risk and generating a prevention and control instruction sequence by comparing the predicted trajectory with the real-time monitoring data.

The invention relates to the technical field of laser cladding repair, and provides a positioning and clamping system and method for repairing the surface of a large working roll of a steel mill based on laser cladding, a three-field coupling prediction model of a temperature field, a stress field and a displacement field is established, and a partial least squares regression algorithm is combined with a Kalman filter to realize multi-step prospective prediction. According to the predicted thermal deformation, self-adaptive compensation control is implemented through a three-layer cascade compensation mechanism composed of a hydraulic drive, a piezoelectric ceramic driver and a laser head adjusting mechanism. The system adopts a time domain, frequency domain and time-frequency domain joint analysis method to extract vibration characteristics, vibration types are classified and identified through a support vector machine, and an active-passive hybrid suppression strategy is adopted for different vibration sources. High-precision positioning and stable clamping of the large working roller in the laser cladding repairing process are achieved, and the thickness uniformity and the surface quality of a cladding layer are remarkably improved.

The invention relates to the technical field of soil moisture content monitoring, and discloses a method and a system for accurately monitoring soil moisture content through remote sensing inversion of an unmanned aerial vehicle. Acquiring original spectral data of different soil types and environmental conditions through a spectral sensor to obtain an initial multispectral image data set; dividing soil types by using a support vector machine classification method according to pixel reflection characteristics to form a soil type grouping set; calculating a multispectral band reflectivity variance for each group, and screening candidate bands to construct a set; processing the candidate wave band set through a random forest regression model, adjusting the weight in combination with an environment variable, and performing iterative screening to form an optimized wave band combination sequence; extracting spectral features of the optimized wave band, and carrying out weighted fusion on the soil variation region to output a processed image; and calculating the soil moisture content by using a linear inversion model based on the pixel reflectivity of the processed image, and outputting a distribution diagram when the deviation reaches the standard. The method is suitable for different soil and environments, the wave band is accurately selected, the precision is improved, and high-quality data is provided for agricultural and environmental governance.

The invention discloses a teaching quality evaluation and optimization platform based on OpenPose skeleton key point detection. The teaching quality evaluation and optimization platform comprises a classroom behavior recognition module, a teaching quality evaluation module and a teaching analysis and optimization module. The classroom behavior recognition module acquires and preprocesses video information generated in a classroom video, then detects skeleton key points in the classroom video by using an OpenPose skeleton key point detection model to recognize teacher and student classroom behaviors, and then performs training based on a support vector machine classification algorithm to generate a classroom behavior classification model to classify the teacher and student classroom behaviors; the teaching quality evaluation module is used for calculating the class positive degree, the class attending seriousness of the students and the teaching seriousness of the teachers and evaluating the teaching quality; the teaching analysis and optimization module carries out teaching analysis and optimization based on teacher and student classroom behaviors and teaching quality evaluation. According to the invention, classroom behaviors of teachers and students can be identified and classified in real time, and comprehensive, objective and reliable teaching quality evaluation is provided.

The invention discloses an equipment operation monitoring and fault diagnosis system based on multi-perception information fusion, and belongs to the technical field of equipment monitoring, and the system comprises the steps: collecting monitoring data in an equipment operation process, and carrying out the preprocessing of the monitoring data, so as to obtain monitoring processing data; obtaining an equipment fault label and corresponding monitoring processing data according to the historical equipment fault record; performing feature extraction on the monitoring processing data, and extracting feature parameters for reflecting the operation state of the equipment; calculating a mutual information value of each feature parameter and the fault tag by using a mutual information method, obtaining key features of which the mutual information values are greater than or equal to a key threshold value, and performing fusion through weighted summation according to a preset weight to form a multi-perception fusion feature; based on the multi-perception fusion features, fault diagnosis is carried out on the equipment by adopting a support vector machine classification algorithm, a diagnosis result is output, and finally the diagnosis result is displayed through a display and early warning module and an early warning signal is sent out.

The invention discloses a state monitoring method and monitoring system for photovoltaic panel waste heat coupling power generation equipment. The method comprises the following steps: acquiring operation original data; classifying and denoising the original data; cleaning and fusing the de-noised data to obtain standardized data; establishing a health index model based on the standardized data, calculating a health index and comparing the health index with a threshold value; when the health index is abnormal, data dimension reduction is carried out, and the fault type and position are determined; system parameters are adjusted or graded alarming is performed according to fault types; through multi-sensor data fusion and dynamic health index evaluation, and in combination with multi-stage fault diagnosis of principal component analysis dimensionality reduction, support vector machine classification and isolated forest detection, the crossing from single parameter alarm to system state comprehensive diagnosis is realized, the fault identification accuracy is improved, and the fault diagnosis efficiency is improved. And the problems of high false alarm rate and difficulty in positioning multiple faults caused by a static threshold and a single algorithm in the prior art are solved, and the operation reliability and the energy recovery efficiency of the waste heat coupling power generation system are remarkably improved.

The invention relates to the technical field of image processing, and discloses a stamping part stamping forming quality detection method, which comprises the following steps: acquiring a surface digital image of a stamping part, analyzing the surface curvature and the material thickness change gradient of the stamping part, and generating a space prior weight map; calculating the structure tensor of a pixel point, and determining the orientation and axial length ratio of an elliptical sampling neighborhood; pixel point sampling is carried out along the elliptical sampling neighborhood to obtain an initial three-valued mode code; a global feature histogram is constructed, the contribution degree is accumulated to the statistical unit, and the contribution degree is the corresponding weight value of the pixel point in the spatial prior weight map; all the global feature histograms are cascaded to generate cascaded feature vectors; and inputting the cascade feature vector into a support vector machine classification model, and judging whether the stamping part to be detected is qualified or not. According to the method, the space prior weight map is introduced, the prior knowledge of the punch forming process is fused into the feature extraction process, so that the detection focuses on the defect high-incidence area, the extracted features are more targeted, and the detection accuracy is improved.

The invention belongs to the technical field of corrosion thickness measurement, and provides a transformer substation reinforced concrete corrosion thickness evaluation method and system based on ultrasonic waves, and the method comprises the steps: determining an ultrasonic parameter combination according to geometrical shape information and spatial position information, and achieving the real-time adjustment of ultrasonic parameters; according to the frequency spectrum characteristic parameters and a preset support vector machine classification model, obtaining a corrosion type; the corrosion thickness and the corrosion degree grade are obtained according to the corrosion type and corrosion degree evaluation model, and the influence of different types and distribution characteristics of corrosion products on propagation and reflection of ultrasonic waves on the surface of the component is solved; through the comprehensive improvement of adjusting ultrasonic detection parameters to comprehensively reflect the overall corrosion condition, considering the geometrical shape and the spatial position of the corrosion part and performing evaluation according to the corrosion type, the evaluation method can adapt to the complex corrosion part, the detection data quality is improved, the comprehensive and accurate evaluation of the corrosion degree of the metal component is realized, and the evaluation efficiency is improved. And a reliable basis is provided for component maintenance and safety assessment.

The invention discloses a portable corn rust disease detection device and method, and relates to the field of corn rust disease detection. The image acquisition module acquires information data of target corn leaves; the main control module performs corn rust disease detection according to the spectral information based on a corn rust detection model, and determines a pixel point detection result; the pixel point detection result is a healthy point, an early disease point or a disease point; the corn rust detection model adopts a support vector machine classification model; performing pseudo-color enhancement processing on the image information by adopting a pseudo-color technology to obtain processed image information; segmenting the processed image information based on a YOLOv5 segmentation model, and determining a target leaf disease rate according to the segmented image information; and determining an overall leaf detection result corresponding to the corn rust disease according to the target leaf disease rate. The method is easy and convenient to operate, and the detection efficiency of the corn rust disease can be improved.

The invention discloses an effective hydrocarbon source rock development condition prediction method and a related device, and relates to the geophysical exploration and comprehensive technical field, the method comprises the following steps: capturing spatial feature processing sequence data through a total organic carbon content prediction model, training a strong complex mapping relation between the total organic carbon content and seismic attributes, and determining the effective hydrocarbon source rock development condition; meanwhile, a support vector machine classification model is utilized to analyze a mapping relation between seismic attributes and lithology data of a virtual well in a research area, intelligent prediction of the total organic carbon content and intelligent judgment and identification of lithology are completed respectively, the generalization ability of the model is improved, regional distribution of the total organic carbon content and the lithology can be predicted more effectively, and the prediction accuracy of the total organic carbon content and the lithology is improved. Therefore, high accuracy is kept in a complex geological environment. And finally, determining the thickness and position of the effective hydrocarbon source rock of the target layer section based on the lithology identification result and the total organic carbon content prediction result, thereby realizing accurate evaluation of the development condition of the effective hydrocarbon source rock.

The invention relates to the technical field of artificial intelligence, in particular to a pavement quality detection method and device based on artificial intelligence, and the method comprises the following steps: obtaining pavement images and load data, extracting crack boundary coordinates through edge detection, calculating a displacement difference to obtain crack distribution, calculating an extension rate in combination with time, and carrying out the weighted analysis of a direction difference. The method comprises the following steps: extracting crack boundary displacement difference, identifying defect evolution parameters, calculating stability and environmental sensitivity coefficients through cross coupling, judging degradation types in combination with load fluctuation, calculating defect grades through classification comparison threshold values of a support vector machine, and outputting a pavement overall quality result. Angle difference variation recognizes evolution direction difference, coupling extension rate and direction consistency depicts stability, environmental sensitivity and load fluctuation linkage calculation is introduced to present external action association, and grading is completed based on parameter and threshold difference to realize dynamic grading recognition of defect development rules.

The invention discloses a fruit and vegetable maturity inspection and classification method based on fruit and vegetable identification, and the method comprises the steps: collecting a fruit and vegetable RGB image and a near-infrared image through a high-definition camera and a near-infrared sensor, carrying out the preprocessing of denoising, enhancement, segmentation and the like, and extracting a fruit and vegetable region; extracting features such as colors, textures and shapes by using a pre-trained convolutional neural network, inputting the features into a support vector machine classification model, judging the maturity of the fruits and vegetables, and classifying the fruits and vegetables into immature fruits, mature fruits and over-mature fruits and vegetables. According to the invention, the method has a self-learning capability, can optimize the classification model through quality inspection feedback, improves the classification accuracy, and provides effective technical support for the quality control and management of fruits and vegetables.

The invention discloses a method for constructing a defect detection model, and a defect detection method and device. The method for constructing the defect detection model comprises the following steps: acquiring a target defect image of a display panel; according to the brightness data of each pixel in the target defect image, constructing a row vector sample and a column vector sample, and respectively carrying out classification labeling on the row vector sample and the column vector sample; constructing a classification model for defect detection, wherein the classification model comprises a support vector machine classification model in a row direction and a support vector machine classification model in a column direction; respectively acquiring a first classification hyperplane of the support vector machine classification model in the row direction and a second classification hyperplane of the support vector machine classification model in the column direction; and generating a defect detection model storing the first classification hyperplane parameter and the second classification hyperplane parameter. According to the method for constructing the defect detection model and the defect detection method and device provided by the invention, local defects in the display panel can be detected.

The invention discloses an intelligent gas alarm method and system, and belongs to the technical field of gas safety monitoring. According to the method, multi-source sensing data of gas, flame, human body and valve states are integrated, and after missing value filling, abnormal value detection and correction, preprocessing of moving average filtering and feature extraction based on a fixed time window, the data are input into a support vector machine classification model for risk level identification and judgment; executing a multi-mode linkage control strategy according to the risk level, the valve state signal and the human body existence state signal; and anonymized feature data generated by the model and corresponding event tags are uploaded to a cloud end for incremental learning and model iterative updating based on weighted security performance indexes. According to the invention, multifunctional collaborative linkage and intelligent decision making are realized, the alarm threshold can be dynamically optimized, and the accuracy, environmental adaptability and long-term self-evolution capability of the system are remarkably improved.

The invention relates to a method for monitoring cerebral apoplexy, a device for monitoring cerebral apoplexy and a storage medium, and the method comprises the steps: collecting physiological parameters such as electroencephalogram (EEG), oxyhemoglobin saturation (SpO2), electrocardiogram (ECG) and blood pressure through wearable equipment or a family health monitoring system, and carrying out data preprocessing, feature extraction and anomaly detection; the EEG signals are processed by utilizing frequency domain characteristics and short-time energy analysis, and the cerebral apoplexy risk is identified by adopting a support vector machine (SVM) classification model. The system can analyze and detect abnormal conditions in real time, and sends out early warning information through a mobile phone App, a short message or a telephone to remind a user to see a doctor in time. The system comprises wearable equipment, a family health system, a hospital platform and a mobile health application, and is suitable for personal health monitoring and centralized monitoring of medical institutions. The real-time performance and accuracy of cerebral apoplexy monitoring are remarkably improved, the user experience is enhanced, the medical response speed is increased, and the method has wide application prospects and practical value.

The invention discloses a self-adaptive air pressure regulation and control system of quartz sand unpowered powder selecting equipment, and relates to the technical field of automatic control. According to the method, wind pressure and airflow disturbance signals are collected, a high-resolution map is generated through Hampel filtering and noise reduction, features are extracted through the convolutional neural network, key influence areas are identified, the problem of unstable wind pressure caused by airflow disturbance in a mountainous area is solved, and the particle separation precision is improved; a natural potential energy distribution matrix is constructed based on building floor height difference, velocity field reconstruction and airflow channel optimization are driven, technological parameters are adjusted in combination with a gradient descent algorithm, precise utilization of natural potential energy is achieved, energy consumption is reduced, uneven efficiency is improved, airflow distribution is simulated through a fluid dynamic model, and sedimentation behaviors are analyzed and predicted through particle trajectory. A support vector machine is used for classifying tracks and generating regulation and control instructions, PID control is used for stabilizing air pressure to form a closed loop, the problems that fine powder is mixed with coarse powder, coarse particles are left and the like are avoided, the equipment blockage risk is reduced, and the product purity stability is improved.

The invention relates to an English text auxiliary teaching method and system based on AI vision. The method comprises the following steps: collecting a dynamic eye movement track, a mouth shape change video stream and micro-expression time sequence data when a student reads, and generating a dynamic behavior feature vector by using a convolutional neural network; and carrying out multi-dimensional matching on the vector and a preset pronunciation standard model, accurately positioning a pronunciation deviation region and an understanding difficulty point, and carrying out classification by virtue of a support vector machine to obtain a learning state label. And extracting a high-frequency deviation mode from the tag, constructing a comprehensive behavior matrix through association of a clustering algorithm and an eye movement backtracking trajectory, calculating a teaching level evaluation value, finally predicting a learning trend and determining a resource allocation weight by combining historical evaluation data and adopting a linear regression model, and optimizing and generating a personalized teaching plan. By adopting the method, pronunciation deviation and understanding disorder in English reading can be accurately positioned, so that the resource allocation weight is adaptive to the individual learning track of students, and a data-driven technical path is provided for English personalized teaching.

The invention belongs to the technical field of wireless charging, and particularly discloses a method based on a magnetic resonance type wireless charging system, which comprises the following steps of: arranging a high-temperature-resistant micro temperature sensor and an array around a magnetic resonance coil curved surface to collect real-time temperature difference data, and integrating environmental noise signals as an initial multi-dimensional parameter set to obtain a preliminary temperature difference data set; according to the preliminary temperature difference data set, applying a support vector machine classification method to separate difference signals between curved surface regions, fusing preset coordinate points in a curved surface geometric parameter correlation model, and determining region sensitivity distribution characteristics; and by adopting a Kalman filtering algorithm, carrying out time sequence smoothing processing on the regional sensitivity distribution characteristics to calibrate offset caused by inaccurate matching of temperature difference data and curved surface geometry, and then obtaining calibrated sensitivity distribution. The invention aims to solve the problem that the influence of temperature distribution difference on frequency matching cannot be accurately identified and adapted under the dynamic temperature change of a complex curved surface coil in the prior art.

The invention relates to an intelligent diagnosis method and system for the health state of a wind turbine generator. The method comprises the following steps: acquiring multi-dimensional operation data including dynamic response, thermodynamic state, electrical parameters and environmental parameters, and constructing a multi-dimensional operation characteristic matrix; utilizing a pre-trained feature screening model based on an attention mechanism to calculate potential fault correlation features; based on a preset fault mode library, performing fault classification processing by adopting a support vector machine classification algorithm to obtain a potential fault mode; calculating a risk score of each fault mode according to a preset risk quantification rule; matching and optimizing the maintenance operation set from a preset maintenance rule base to form a wind power maintenance project; and determining a maintenance priority according to the risk score and generating a maintenance priority sequence. By adopting the method, accurate fault identification, risk quantification and maintenance decision intelligence of the wind turbine generator can be realized, and the operation and maintenance efficiency and the equipment reliability are improved.

The invention provides a mental disease classification method and system based on an individual difference structure covariant network and machine learning, and belongs to the field of mental disease classification. The problem of classification performance bottleneck caused by heterogeneity of mental diseases in the prior art is solved. The method comprises the following steps: acquiring a structural magnetic resonance T1 weighted image, and preprocessing the image; performing brain region segmentation on the pre-processed T1 image based on an AAL brain map, and extracting the gray matter volume of each brain region; constructing an IDSCN network by calculating the Pearson's correlation coefficient of the brain grey matter volume of the paired brain regions; calculating the area under a node topological attribute curve of the IDSCN network; screening node attribute indexes with statistical differences between the patient group and the healthy control group through double-sample t test; and taking the screened node attribute indexes as feature vectors, and inputting the feature vectors into a support vector machine classification model for disease classification. The method is mainly used in the medical image processing field.

The invention belongs to the technical field of medical image processing and neural image analysis, and particularly discloses a cerebral apoplexy prognosis prediction method which comprises the following steps: step 1, performing whole cerebral vessel segmentation and morphological feature extraction on a CTA image to obtain 40 blood vessel morphological features; 2, extracting high-throughput omics characteristics from an infarction core and a half-dark band region in the DWI image, and obtaining 1026 image omics characteristics; 3, screening high-resolution features based on minimum absolute contraction and a selection operator, establishing a support vector machine classification model, and predicting a prognosis classification result of a three-month improved Rankin scale of the stroke patient; the deep learning network is used for automatically segmenting the whole brain blood vessel, so that subjective difference of manual recognition is reduced; starting from pathophysiology of occurrence and development of cerebral apoplexy, vascular morphological characteristics and infarction region imageomics characteristics are comprehensively incorporated to carry out prognosis prediction on cerebral apoplexy.

The invention relates to a food multi-modal detection data fusion analysis method and device, equipment and a medium. The method comprises the following steps: acquiring original multi-source heterogeneous data of a food spectrum, an image and a smell, and performing normalization and noise reduction preprocessing to obtain a standardized feature set; classifying and screening high-correlation features by using a support vector machine, and generating a structured description; dynamic parameters are extracted, a quality trend vector is generated through time sequence analysis, a safety score is calculated, and a qualified mark is output if the safety score reaches the standard; constructing an extension index set based on the score, inputting a dynamic model to generate an authentication update link, and combining the block chain time to obtain new-version authentication; and associating the historical records to generate an initial report, and verifying and correcting to obtain an optimized report. By adopting the method, the consistency and availability of food safety data can be improved, the credibility of safety certification is enhanced, and a systematic and efficient solution is provided for food quality safety detection.

The invention relates to the technical field of data processing, in particular to a geological disaster occurrence trend prediction system based on historical data, which comprises an anomaly recognition module, a clustering labeling module, a stress partitioning module, a trend classification module and a disaster prediction module. According to the method, by introducing a multi-level abnormal data analysis and clustering technology, potential abnormal changes can be recognized more accurately when multi-dimensional data are processed, and therefore the problem that non-linear and dynamic changes of data are neglected in a traditional method is effectively solved. By calculating the difference of the multi-dimensional data, different stress partition areas can be distinguished, trend prediction is performed based on the characteristics of the areas, and the accuracy and timeliness of disaster prediction are remarkably improved. A support vector machine classification method is utilized to integrate focusing features and stress features, not only is the adaptability of a prediction model optimized, but also a prediction result can be adjusted according to real-time data, so that support with higher timeliness and reliability is provided for decision making.

The invention discloses a bicycle chain wheel surface spraying quality detection method, which is characterized in that a target area of an image is rapidly and accurately detected through a YOLOv3 model, and then texture feature information of a bicycle chain wheel spraying surface is extracted by using a Gabor filter. The method can effectively distinguish surface features of different particles and capture fine texture changes. And finally, inputting the extracted texture features into a support vector machine (SVM) classification model for training and classification, thereby realizing accurate evaluation of the spraying quality. Compared with a traditional manual detection method, the method has the advantages that the detection precision and consistency are remarkably improved, personal errors are avoided, the method is particularly suitable for the complex situation that the particle size and the surface roughness are difficult to distinguish, and more objective and accurate spraying quality judgment is provided.