244 results about "Morphological analysis" patented technology

The invention provides a road bridge crack detection method and system, and the method comprises the steps: collecting a bridge surface multi-view image, and constructing a training data set containing crack feature labeling through quality screening and standardized labeling; preprocessing the image by using a multi-scale feature fused deep convolutional neural network and carrying out semantic segmentation, initially identifying a suspected crack region and generating a segmentation mask; and constructing a BeNNS proxy model based on the mask, and establishing a mapping relationship between the detection result and the bridge structure topology, the stress flow field and the service function chain so as to evaluate the result reliability. And inputting an evaluation result into a hybrid evaluation mechanism, performing online real-time detection and offline batch verification to optimize precision, and outputting a verified crack region. Finally, morphological analysis is conducted on the area, geometric parameters and danger levels of cracks are extracted and integrated to a bridge health monitoring system, a crack evolution tracking algorithm and an early warning mechanism are established, and dynamic tracking early warning is achieved. The problem of low detection precision in a complex environment can be solved.

The invention relates to the technical field of intelligent agriculture, and discloses a visual intelligent agricultural planting control system. The system comprises an environment data acquisition module used for acquiring multi-source environment sensing data; the growth feature modeling module is used for extracting crop growth state features through a morphological analysis algorithm; the environment regulation and control decision module is used for generating an environment regulation and control instruction set by utilizing a dynamic threshold matching algorithm; the visual interaction module is used for generating a three-dimensional farmland live-action simulated diagram through multi-dimensional data fusion processing; the strategy execution module is used for driving agricultural facilities to execute actions by adopting a self-adaptive control algorithm; an abnormity early warning module is further arranged, and abnormity early warning signals are generated through correlation analysis. The system realizes comprehensive acquisition and analysis of agricultural planting environment data, precise environment regulation and control, visual and visual display and abnormal early warning, effectively improves the intelligent and precise level of agricultural planting, improves the crop yield and quality, and assists the development of intelligent agriculture.

The invention relates to the technical field of image processing, in particular to an intelligent fracture diagnosis system based on image recognition, which comprises an image analysis module, a mode recognition module, a form analysis module, a risk assessment module and an auxiliary decision module. According to the method, skeleton gray level distribution and boundary consistency are analyzed through continuous frames of X-ray images, fracture feature extraction precision and time sequence coherence are improved, key point space distribution, symmetry standards and form proportions are fused, fracture area structured quantitative evaluation is achieved, the form change trend and abnormal offset point screening are combined, and the accuracy of fracture feature extraction is improved. The method enhances abnormal trajectory recognition precision, associates bone mineral density and form offset, calibrates high-risk time periods, improves risk assessment perspectiveness and individual adaptability, dynamically corrects output content according to an inter-frame suggestion change trend and extended feedback, enhances timeliness of diagnosis suggestions and closed-loop feedback quality, and improves risk assessment accuracy. Image evolution, structural geometry and physiological data are integrally fused, and multi-dimensional intelligent judgment of fracture recognition and evaluation is achieved.

The invention discloses an abnormal monitoring method and system for immune cell culture. The method comprises the following steps: collecting cell physical state parameters, culture environment parameters and metabolic biochemical indexes in a culture system in real time; performing motility rate threshold judgment and morphological analysis based on the cell physical state parameters to generate a first abnormal signal; performing dynamic trend analysis on the metabolism biochemical indexes to generate a second abnormal signal; performing grade association on the culture environment parameters, the first abnormal signal and the second abnormal signal, and outputting a monitoring abnormal grade; and performing grading response construction according to the monitoring abnormity grade to obtain an abnormity monitoring report. According to the method, the pollution diffusion risk and the functional cell failure misjudgment rate can be reduced.

The invention relates to the field of geographic information technology and natural resource management, and discloses an automatic analysis system for land utilization flow change in land change investigation. The system comprises an object-level spatiotemporal spectrum tree reconstruction and data initialization module, a topological potential energy field construction module based on evolution entropy, a multi-scale geometric difference extraction and morphological analysis module, a variable stiffness topological adsorption and decoupling module based on a stiffness ratio, and a flow damping filtering and event serialization module driven by semantic rules. The system constructs a topological potential energy field by using a historical state vector and semantic stiffness, performs one-way adsorption, adjustment or retention on a difference polygon according to relative stiffness, and cleans invalid traffic based on a damping matrix. According to the method, through physical field simulation and semantic logic verification, high-frequency disturbance misjudgment is effectively eliminated, pattern spot boundary correction is ensured to conform to the legal weight, and the authenticity and logic self-consistency of change survey data are improved.

The invention discloses a control method and system of a servo press, and relates to the field of press control, a force displacement curve is obtained in real time from a current punching period, a key feature vector of the force displacement curve is extracted, the key feature vector is compared with an ideal golden feature vector, and a multi-dimensional deviation vector is obtained. Particularly, a deviation decoupling method based on morphological analysis is introduced, and the total deviation can be accurately decomposed into a thermal drift compensation amount caused by the thermal effect of equipment and a material characteristic compensation coefficient caused by plate performance fluctuation by analyzing translation and zooming differences of a current curve and a gold curve in form. Therefore, independent and accurate compensation can be carried out for a physical source, the problems of information aliasing and compensation failure caused by the fact that disturbance sources cannot be distinguished in the prior art are effectively solved, intelligent and feed-forward type cooperative compensation for the next stamping period is achieved, and long-term stability and high precision of the production process are ensured.

The invention relates to the field of image recognition, in particular to an invader monitoring method and system based on laser and video image fusion. The method comprises the following steps that real-time laser scanning monitoring signals are collected based on a multi-array laser, dynamic area laser shielding disturbance excavation is carried out, and a working area foreign matter light beam shielding disturbance map is constructed; the method comprises the following steps: acquiring a working area monitoring video stream according to a high-definition camera, performing adaptive cache optimization and foreign matter invasion key frame segmentation, and extracting a foreign matter invasion video frame sequence; carrying out light beam shielding density calculation according to the light beam shielding disturbance map, and carrying out three-dimensional morphological analysis on the foreign matter invasion video frame sequence to obtain foreign matter three-dimensional morphological characteristics; and performing dynamic optical flow tracking and vector change frequency analysis on the foreign matter invasion video frame sequence, and constructing a foreign matter disturbance trajectory vector field. According to the invention, efficient and accurate invader monitoring is realized, and the operation safety and quality are improved.

The invention discloses a cerebral apoplexy prediction method and device based on a cerebrovascular image representation group, a medium and a product, and relates to the field of image processing, and the method comprises the steps: constructing a clinical database; according to the multi-modal image feature group data, the clinical feature data and the clinical cause typing of the patient, constructing a clinical cause typing prediction model; according to TOF-MRA, a cerebrovascular tissue segmentation model and voxel-based morphological analysis are adopted to determine cerebrovascular image features, and positioning diagnosis of a responsible blood vessel area is carried out according to the cerebrovascular image features; according to the cerebrovascular image features in the TOF-MRA, the SWI, the T1WI, the T2-FLAIR and the corresponding clinical feature data, a cerebral apoplexy recurrence risk prediction model is constructed; according to the application, the pathogenesis classification of the acute cerebral apoplexy patient can be quickly and accurately realized in a clinical scene, the responsible blood vessel can be accurately identified, and the recurrence risk prediction can be realized in the early stage of the disease.

The invention relates to the technical field of biology, in particular to a molecular marker for accurately positioning centromere of chromosome of Chinese trumpet creeper, and the sequence of the molecular marker is as shown in SEQ ID NO.1 and SEQ ID NO.2. The method comprises the following steps: specifically enriching a DNA (Deoxyribose Nucleic Acid) sequence combined with a centromere specific histone CENH3 through a chromatin co-immunoprecipitation technology (ChIP), and sequencing the DNA sequence; sequencing data are analyzed to obtain a centromere sequence of the vicia villosa, and two specific repetitive sequences of the centromere of the vicia villosa are obtained after identification and optimization. Primers are designed according to the sequences, PCR amplification is carried out, amplification product sequences are marked, fluorescence in situ hybridization verification is carried out by analyzing a cytogenetics technology, it is proved that the primers are specifically distributed in a centromere area of vicia villosa, and the centromere specificity of the primers is proved. The marker can be used for genetic identification and accurate positioning of the centromere in genome research, and can also be used for cytological identification of the centromere and karyotype research such as chromosome counting and morphological analysis through centromere signal observation.

The invention relates to the technical field of unit detection, and discloses an intelligent fault self-diagnosis method for an evaporative condensation integrated screw water chilling unit, which comprises the following steps of: decoupling operation data of a compressor through a blind source separation algorithm, performing image processing on operation data of a condenser in combination with a morphological analysis method, and performing fault diagnosis on the operation data of the condenser; the operation characteristics of the compressor and the operation characteristics of the condenser can be extracted respectively, effective decoupling and clear expression of coupling signals are realized, and by constructing a dual-channel LSTM network comprising a gating fusion unit and introducing a cross-channel coupling gating mechanism, the system reliability is improved. The dynamic coupling relation between the compressor and the condenser can be modeled and regulated in the time dimension, the coupling mechanism between the compressor and the condenser is truly reflected, the time sequence parameter sequence is input into the pre-trained deep LSTM network, the fault types such as control lag and performance degradation can be accurately recognized, and the fault diagnosis accuracy is improved. Therefore, the accuracy and real-time performance of fault diagnosis are improved, and the requirement for intelligent fault management of the water chilling unit is met.

The invention relates to the technical field of image analysis, in particular to a temporal bone and gallbladder sebaceous tumor detection method and system based on multi-modal medical image fusion. The method comprises the following steps: acquiring a multi-modal temporal bone and gallbladder sebaceous tumor image of a patient, carrying out multi-modal image time sequence space alignment, carrying out feature vector change rate fitting, and constructing a multi-time-point image feature vector map; performing multi-modal image voxel decomposition on the multi-time-point image feature vector map, and performing three-dimensional morphological analysis to generate a geometric morphological parameter set; carrying out potential pathological hierarchical structure mining based on the geometric morphology parameter set, carrying out semantic label labeling, and constructing a multi-modal lesion semantic labeling image; and carrying out multi-time-point image change analysis on the multi-modal lesion semantic marking image, and carrying out parallel lesion evolution state prediction so as to generate a cholangioma evolution state prediction map. According to the method, accurate positioning, effective segmentation and visualized display of the temporal bone and gallbladder sebaceous tumor focus are realized.

The invention discloses a skull data comparison method based on artificial intelligence. The skull data comparison method comprises the following steps: S1, acquiring three-dimensional scanning data of a skull to be detected and a reference skull database; s2, preprocessing the skull data to be detected; s3, multi-dimensional features of the skull to be detected are extracted through the deep learning model; s4, performing similarity comparison on the skull features to be detected and skull features in the reference database to generate a matching degree score; s5, outputting identity information or a morphological analysis result of the target reference skull according to the matching degree score; the invention further provides a skull data comparison system based on artificial intelligence. The skull data comparison system comprises a data acquisition module, a preprocessing module, a feature extraction module, an intelligent comparison module and a visual output module. Compared with the prior art, the method has the advantages that the comparison accuracy is high; the processing speed is high; the application range is wide.

The invention discloses a power equipment infrared detection method based on an image, and relates to the technical field of image detection, and the method comprises the steps: obtaining an infrared thermal image, a visible light image and a vibration data set of to-be-detected equipment; establishing a displacement prediction model to obtain an alignment degree and a first stain image; calculating vibration energy density according to the vibration data set, and calculating thermal gradient variance according to the infrared thermal image; according to the first stain image morphological analysis and the vibration energy density, obtaining an adhesion index; determining a main boundary and a diffusion boundary of the stain region based on the adhesion index and the thermal gradient variance, evaluating an influence value of the cross region, and determining a generation strategy based on the influence value to obtain a second stain image; vibration data are collected in real time, dynamic changes of equipment and stains in a vibration mode are effectively recognized, and image fusion is dynamically regulated and controlled according to the changes; and a plurality of alignment indexes are defined, so that the accuracy of stain image recognition is further improved, and the precision of equipment monitoring is improved.

The invention relates to the technical field of geological survey, in particular to an unmanned aerial vehicle multispectral geological survey method and system, and the method comprises the steps: fusing a multispectral image, a digital elevation model, geophysics and historical geological data, systematically constructing a geological feature priori knowledge model, including lithology, construction and alteration feature libraries, and mapping with multispectral data; multi-scale geologic features are extracted through adaptive wavelet transform and morphological analysis, and feature weight adaptive adjustment is achieved; geological units are accurately divided by adopting geological scene perception superpixel segmentation and combining geological boundary constraint and similarity recursion combination; identifying an interference mode, generating an adaptive filtering matrix, and enhancing image quality; cooperatively interpreting multi-source information by using a deep auto-encoder network to generate a high-precision geological interpretation map and a confidence map; geological professional knowledge is introduced, so that the geologic body recognition accuracy is remarkably improved; the adaptive flight control strategy ensures the consistency of complex terrain data, and improves the precision and efficiency of geological survey.

The invention discloses a ceramic censer design method and system based on perceptual engineering, and belongs to the technical field of neural networks, and the method comprises the steps: obtaining and preprocessing a ceramic censer sample, and building a morphological analysis table which comprises a plurality of morphological feature vectors of the ceramic censer; establishing a plurality of positive adjectives based on the user comment text; performing cloud model parameter matrix operation on the plurality of frontal adjectives to obtain sentiment word weight vectors, and generating a Kansei evaluation matrix table; the morphological analysis table is input into a preset TCN-Transform-GRU model, and an optimal product morphological design combination is obtained; the optimal product form design combination is input into the preset stable diffusion model, and the ceramic censer rendering graph is obtained.Design information is efficiently expressed through the cross-modal generation technology, evaluation and optimization are conducted through a quantification method, and the generated ceramic censer image is more real and more exquisite.

The invention relates to the technical field of disease monitoring, in particular to a disease monitoring method and system in the epimedium growth process, and the method comprises the steps: deploying a multi-source collection device in an epimedium planting area, and obtaining plant images in real time according to a preset time interval, the plant images including RGB images and multispectral images; performing image preprocessing on the acquired RGB image to obtain a standard plant image; acquiring plant growth time, performing morphological analysis on the standard plant image to extract growth characteristic parameters, and inputting the growth characteristic parameters and the growth time into a pre-constructed plant growth stage prediction model to acquire the epimedium growth stage. According to the disease monitoring method for the epimedium growth process provided by the invention, by fusing RGB and multispectral images, accurately extracting key features such as diseased leaves and areas, colors and shapes of the diseased leaves, and inputting a disease prediction model in a growth stage, intelligent discrimination of epimedium disease types and severity levels can be realized.

The invention relates to the technical field of industrial vision, in particular to an automobile part surface defect detection method and system based on machine vision, and the method comprises the steps: firstly loading a CAD three-dimensional model, and rendering an ideal fringe reflection map in a virtual environment; calculating an optical distortion correction matrix by comparing the phase distortion with the phase distortion of an actual initial reflection image, driving a programmable light source to project a compensation pattern, and generating a normalized reflection intensity image; signal object decoupling is achieved through multi-scale wavelet transform, and high-frequency components and low-frequency components are separated; homomorphic filtering is applied to the low-frequency component to construct a homogenized background model, the high-frequency component is reversely corrected, and a high-signal-to-noise-ratio defect signal image is output; generating a coarse segmentation mask by the high-frequency signal through an adaptive local threshold, generating a morphology anomaly mask by the low-frequency signal through Hessian matrix morphological analysis, and fusing to form a collaborative segmentation mask; and extracting multi-dimensional geometric attributes of connected domains in the masks, and inputting the multi-dimensional geometric attributes into a decision tree to realize accurate classification and confidence output of defects.

The invention relates to the technical field of traditional Chinese medicine recognition, in particular to a traditional Chinese medicine recognition system based on an image edge detection algorithm, which can effectively extract edge information of a traditional Chinese medicine image, reduce noise interference and ensure that key information in the image is accurately reserved, and has excellent image edge detection capability. And the method also has strong feature extraction and classification identification capabilities. In the feature extraction module, the system extracts multi-dimensional information such as shape features, edge contours, texture features and color distribution by performing morphological analysis on edge images.

The invention relates to the technical field of reproductive medicine, in particular to a high-quality sperm acquisition method based on a testis tissue sample of an azoospermia patient, which comprises the following steps: inputting a first frame of a testis tissue sample microscopic video sequence into a target detection model, outputting a sperm detection frame and confidence, and taking the position of the detection frame as a starting point of a motion trail; a dynamic confidence matching strategy is adopted, firstly, a prediction trajectory is matched with a high-confidence detection result, then, a real leak detection target is complemented with a low-confidence target, multi-stage Hungary matching is achieved through confidence guidance, and all sperm movement trajectories obtained in each frame are obtained; and based on multiple frames of images corresponding to all sperm movement tracks corresponding to the microscopic video sequence, selecting the multiple frames of images according to a preset number of confidence coefficients to construct a candidate morphological analysis sample set, and performing kinematics parameter calculation and morphological analysis to obtain high-quality target sperms. According to the method, the recognition efficiency of high-quality sperms is improved, the sperms with higher quality can be obtained, and the subsequent fertilization success rate is further improved.

The invention relates to the technical field of data expansion, in particular to a malicious prompt data set expansion method based on Mongolian. According to the method, high-frequency roots and affixes in a Mongolian basic malicious prompt corpus and a universal corpus are extracted, morphological analysis is combined, targeted malicious prompt samples can be constructed, the diversity and authenticity of a Mongolian safety evaluation data set are enhanced, candidate malicious prompt samples are optimized by adopting a genetic algorithm, and the safety evaluation accuracy of the Mongolian safety evaluation data set is improved. According to the method, the expansion sample is enabled to better conform to syntax and semantic rules of the Mongolian, effectiveness of the attack sample in the Mongolian scene is ensured, the expansion data set is enhanced by using the antagonism generation strategy, robustness and antagonism of the data set are improved, attack behaviors possibly occurring in a real scene can be simulated, and the robustness and the antagonism of the data set are improved. Therefore, an effective sample generation tool is provided for safety evaluation in a Mongolian scene, and the anti-attack capability of the Mongolian model is improved.

The invention relates to the field of image processing, in particular to a detection, morphological analysis and distance measurement method and system for a ponding area. Obtaining a road image; detecting the obtained road image by using a pre-trained ponding detection model to obtain an image containing a ponding area; inputting the image containing the ponding area into a pre-trained UNet3 + network for segmentation to obtain a ponding area mask map; performing morphological analysis on the ponding area mask pattern to obtain characteristic parameters of the ponding area; and carrying out distance measurement processing on the image containing the ponding area based on a parallax method to obtain the distance between the ponding area and the vehicle. According to the method, the ponding detection model is combined with the UNet3 + network and parallax method distance measurement, so that the road ponding detection precision, the segmentation efficiency and the distance measurement reliability are remarkably improved, and the problems of limited detection range, insufficient feature extraction and lack of spatial information in the prior art are solved.

The invention discloses a method for identifying defects of an underground energy storage space structure, and the method comprises the steps: obtaining the point cloud data of the underground energy storage space structure, and constructing a three-dimensional point cloud model based on the point cloud data; performing feature extraction on the three-dimensional point cloud model to obtain a crack region; calculating the crack area to obtain occurrence information and a group system of the crack; carrying out refinement processing on the crack region through a three-dimensional morphological analysis method to obtain a crack skeleton line, and carrying out length calculation on the skeleton line to obtain a crack continuity parameter; according to the crack skeleton line, carrying out width calculation on the crack region to obtain width distribution characteristics; obtaining filling corrosion characteristics according to the point cloud reflection intensity information of the crack area; evaluation is carried out according to occurrence information and groups of the cracks, crack continuity parameters, width distribution characteristics, filling dissolution characteristics and density information, a comprehensive score is obtained, and an underground energy storage space structural body defect degree identification result is obtained according to the comprehensive score.

An information processing method includes: obtaining, as text information, information related to communication by a person; performing emotion analysis on a plurality of words or phrases after breaking down the text information into the plurality of words or phrases by performing morphological analysis on the text information; and visualizing an analysis result of the emotion analysis according to each row and column of a matrix table by forming the matrix table by arranging, in a first direction, a plurality of emotional expression-related items that represent human emotions and arranging, in a second direction, an attribute category-related item that indicates an attribute of the person or an organization.

The application discloses a recruitment interview evaluation method and system based on artificial intelligence and a storage medium, relates to the technical field of recruitment interview evaluation, and solves the technical problem that the prior art only completes morphological analysis and pragmatic reasoning through voice recognition and corpus comparison, does not deeply mine dynamic characteristics of voice signals, and does not perform correlation analysis on personality descriptions in resumes and real-time emotional performances in interviews, so that the evaluation dimension is single, and it is difficult to comprehensively and objectively reflect the language expression ability, psychological state and personality matching degree of a job seeker; the application obtains voice data and emotional data of a job seeker; the voice data is preprocessed to obtain a formant sequence corresponding to the voice data; a voice evaluation coefficient is calculated based on the formant sequence; an emotional similarity is calculated based on the emotional data; an interview evaluation coefficient is calculated based on the voice evaluation coefficient and the emotional similarity; and whether the job seeker is qualified is judged based on the interview evaluation coefficient, so that the above technical problem is solved.

The invention discloses a design method of a structural mechanical gradient regulation and control interbody fusion cage for end plate defects. The design method comprises the following steps of data acquisition and model construction; analyzing the shape of the defective end plate; carrying out interface mechanical adaptation design on the defect end plate and the structural gradient fusion cage; performing multi-objective optimization; the invention relates to a 3D printing defect endplate mechanical and biological adaptive spinal fusion cage. The individual end plate defect model can be established according to the difference of different patients, the local elastic modulus of the fusion cage is dynamically adjusted through morphological analysis and typing, the stress conduction height matching with the defect end plate is realized, the settlement risk is reduced, and the fusion effect is improved; by combining the biological adaptation design of the end plate, the pore structure and material characteristics of the fusion cage are optimized, and bone healing is promoted; through the scheme of image analysis, digital design, intelligent optimization and precise manufacturing, a mechanical and biological dual-adaptive fusion cage system solution is provided for a patient with the defect of the end plate.

The invention relates to a water treatment system and method based on computer vision and multi-agent reinforcement learning. The water treatment system based on computer vision and multi-agent reinforcement learning comprises a multi-source sensing module used for synchronously acquiring a mixed solution image sequence of an A2O process aerobic tank and sensor data of each process unit; the feature extraction and fusion module is used for performing semantic segmentation and morphological analysis on the image sequence, extracting microbial visual feature parameters, and fusing the visual feature parameters with sensor data to generate a fusion state vector; the intelligent decision-making module is a module based on a multi-agent reinforcement learning model and is used for outputting a combined control action vector according to the fusion state vector; and the execution control module is used for adjusting the aeration rate, the internal reflux ratio and the external reflux ratio of the A2O process according to the combined control action vector. According to the water treatment system disclosed by the invention, a complex biochemical process is realized, closed-loop control of multi-target collaborative optimization is carried out, the treatment efficiency is improved, and the energy consumption is reduced.

The invention relates to the technical field of machine learning, in particular to an intelligent sorting method and system based on machine learning, and the method comprises the steps: carrying out the image collection processing of a to-be-sorted article through an industrial camera, and generating an original image data set containing the article contour distribution information, performing multi-dimensional morphological analysis processing on the original image data set to obtain a morphological feature set of the to-be-sorted articles; calling a feature extraction network to perform multi-level feature extraction processing on the morphological feature set to obtain a target article feature vector; the target article feature vectors are input into a target classification decision model, a category affiliation decision result of the to-be-sorted articles is generated, a sorting decision instruction is generated based on the category affiliation decision result, and the sorting decision instruction is used for instructing a sorting robot to execute sorting operation on the to-be-sorted articles; and a mechanical arm in the sorting robot is controlled to execute sorting operation based on the sorting decision instruction, and the to-be-sorted objects are transferred into a logistics container corresponding to the target sorting area.

The invention provides an alumen ustum form monitoring method based on optical detection, belongs to the technical field of water treatment, and solves the problems that impurities such as silt, organic matters, algae and the like and bubbles generated in a stirring process can be overlapped with alumen ustum in an image, so that a target cannot be accurately distinguished during image segmentation, and the image quality is influenced. Therefore, the technical problem of misjudgment of the form parameters of the alumen ustum is solved. An alumen ustum form monitoring method based on optical detection comprises the following steps: constructing an impurity and bubble optical characteristic database of a multispectral light source, scanning a three-dimensional space of a water sample to obtain optical signal data, carrying out multi-characteristic fusion analysis to extract optical characteristics of impurities and bubbles, and establishing a multispectral recognition model to classify and distinguish various substances. And finally, accurately segmenting the alumen ustum area and extracting morphological parameters of the alumen ustum area According to the method, various substances can be accurately distinguished, the alumen ustum recognition and morphological analysis accuracy is improved, reliable data support is provided for automatic control and optimization of a water treatment process, and water quality safety is guaranteed.

The invention relates to the technical field of image processing, and discloses a vibrating screen mesh mesh state monitoring method based on image processing, which comprises the following steps of: acquiring a continuous image sequence of a screen mesh through a fixed camera, dividing analysis areas, generating a brightness oscillation time sequence of each area, adaptively extracting a reference oscillation sequence representing the overall vibration beat, and calculating the brightness oscillation time sequence of each area; according to the method, a dynamic image sequence is converted into time sequence signal analysis, early anomaly detection is achieved by capturing phase asynchronism of brightness oscillation, limitation of traditional static form analysis is avoided, meanwhile, fault type distinguishing is achieved based on harmonic characteristics, and the fault detection accuracy is improved. And a vibration signal is multiplexed to carry out early warning on the health state of the whole machine, so that the detection reliability and the maintenance efficiency are improved without physical mark points.

The invention provides a false tooth image intelligent identification method fusing defect morphological characteristics, and relates to the technical field of computer vision and medical image processing. The false tooth image intelligent identification method fusing defect morphological characteristics comprises the following steps: performing multi-scale morphological operation on a false tooth image to generate a multi-scale morphological characteristic graph; the morphological features and the deep learning features are fused, and a defect area is highlighted through an attention module; using a pre-training model and a data enhancement strategy to improve the small sample defect identification capability; and outputting a defect category probability and a positioning thermodynamic diagram. Through multi-scale morphological analysis, a feature fusion network and an attention mechanism, high-precision identification of the false tooth surface defects is realized. Experimental results show that the F1 value of the method on a public data set reaches 0.92, the method is remarkably superior to an existing method, and the method is suitable for automatic quality detection in the false tooth production process.