1834results about How to "Efficient detection" patented technology

The invention discloses a textile product defect detection method and system, and the method comprises the steps: collecting a textile surface image, and carrying out the preprocessing; constructing a feature extraction model, and extracting multi-modal features of the textile fabric surface defects; analyzing the extracted features, and classifying defect types; positioning defect positions, and marking defect areas and types; and outputting a defect detection report, and optimizing model parameters according to a detection result. Through multi-modal feature extraction and a feedback optimization mechanism, the classification accuracy and the positioning precision of textile fabric defect detection are remarkably improved, and particularly, the method has outstanding performance in detection of complex and low-contrast defects. According to the invention, the detection efficiency is greatly improved, false detection and missing detection are reduced, and an efficient and intelligent solution is provided for quality control of the textile industry.

The invention relates to an aviation turbine blade service damage monitoring method based on digital twinning, belongs to the technical field of aero-engines, and solves the problems that an existing detection algorithm is high in calculation complexity and low in efficiency and precision. Determining a to-be-monitored turbine blade physical entity, and collecting turbine blade environment information; a digital twin is constructed on the basis of a physical entity, the turbine blade three-dimensional model construction module constructs a turbine blade three-dimensional model according to the physical entity, and the turbine blade operation environment simulation module performs environment simulation on the model according to real-time load, rotation speed and environment temperature data of a turbine blade. And the turbine blade defect evolution prediction module identifies blade surface defects of the model at each moment by using an intelligent defect detection algorithm to obtain the category, position information and size information of the defects, and performs evolution simulation and danger coefficient analysis. According to the invention, real-time on-line, high-precision and high-efficiency detection of the surface defects of the aviation turbine blade is realized, and the service life of the turbine blade is prolonged.

The invention discloses a power system false data injection attack detection method and system. Measurement data of a power distribution system are acquired; constructing an attack sequence, and injecting the constructed attack sequence into the obtained measurement data to simulate a false data attack; constructing a false data injection attack detection model based on the expansion causal convolution and the recurrent neural network to analyze the state change of the power distribution system; and judging an attack behavior according to the analyzed state of the power distribution system. According to the method, time sequence data can be processed, the long-term dependency relationship and the complex mode can be captured, and the response capability to the complex attack mode can be improved, so that the false data injection attack can be effectively detected and prevented, and the safety and reliability of the power distribution system can be maintained.

The invention relates to the technical field of molecular markers, and discloses an SNP (Single Nucleotide Polymorphism) molecular marker related to the litter size character of pigs and application of the SNP molecular marker. The SNP molecular marker is located at a 34977540bp site of a chromosome 2 of an international pig reference genome Sscrofa11.1 version, and the nucleotide sequence of the site is G or A and is located in an exon region of an LUZP2 gene. The invention also discloses an application of the SNP molecular marker in breeding of the litter size character of the boar, and provides a method for breeding or auxiliary breeding of the boar with high / low litter size based on the marker. The invention also provides a specific primer for identifying the SNP site. By applying the technologies, accurate prediction and efficient screening of the litter size of the large white pigs can be achieved, the breeding efficiency of excellent breeding pigs is remarkably improved, and the population improvement process is accelerated. The invention also provides important technical support for sustainable development of pig raising industry, also provides theoretical basis for research in related fields, and is expected to promote further development of pig genetic improvement work.

The invention relates to the technical field of glass detection, and discloses a method, a device and equipment for detecting glass after light sweeping. The method comprises the following steps: collecting multi-source original sensing data of glass to be detected, carrying out signal denoising, synchronous transformation and multi-scale glass feature extraction on the multi-source original sensing data, and carrying out feature combination, feature dimension reduction compression, region segmentation and region contour reconstruction on the multi-scale glass features to obtain an initial defect region set; performing spatial-temporal feature combination of multi-source features on the initial defect region set to obtain an enhanced defect feature set; and constructing a region similarity matrix corresponding to the enhanced defect feature set, performing defect category judgment and multi-dimensional glass quality scoring on the region similarity matrix to obtain glass quality evaluation data, performing process association on the glass quality evaluation data, and generating a final detection report of the to-be-detected glass. According to the method, the result accuracy of quality detection on the glass subjected to light sweeping is improved.

The invention discloses a power battery fault detection method based on a bidirectional Mama architecture, and the method comprises the steps: firstly collecting the voltage, current, temperature and internal resistance of a power battery as original data, and carrying out the preprocessing of the original data, and generating a data set; secondly, the bidirectional Mama feature extraction network and the weighted reconstruction network are connected in series to construct a bidirectional Mama network model, bidirectional representation modeling of the time dimension and the feature dimension is carried out on the power battery data in the data set, and reconstruction output is carried out through the weighted reconstruction network. And finally, taking the time sequence data of the power battery under the normal working condition as the input of the bidirectional Mamba network for training. And according to a reconstruction error between the output data and the input data of the weighted reconstruction network, a fault threshold is set, and fault detection is realized. The key information in the time sequence data of the power battery is accurately extracted, potential faults in the power battery are efficiently and comprehensively detected, and the safety of the power battery is improved.

The invention relates to the technical field of image analysis and processing, in particular to a high-precision detection method based on image analysis, which comprises the following steps: acquiring an original image to be detected, performing preprocessing operation on original image data, including normalization processing, de-noising processing and brightness adjustment, performing multi-scale feature conversion on the preprocessed image to generate feature matrixes of different scales, calculating the correlation degree between the feature matrixes through a correlation self-adaptive fusion module, and generating a fusion feature map; calculating a significance score of each pixel point in the fused feature map, generating an abnormal score matrix, and determining a significant abnormal region in combination with the deviation matrix; original image information is decomposed into different scales through a multi-scale feature conversion module, local and global abnormal features of an image are captured by using comprehensive information of feature matrixes of different scales, information redundancy among the features of the scales is eliminated, and the identification degree and the detection accuracy of an abnormal region are improved.

The invention discloses a power transmission line strain clamp electrified X-ray digital imaging detection device which comprises an X-ray machine, an imaging plate matched with the X-ray machine and a carrying unmanned aerial vehicle, an equipment frame is arranged at the lower end of the unmanned aerial vehicle, a walking mechanism is arranged on the equipment frame, and an undercarriage, a camera shooting mechanism and a power source are arranged at the bottom of the carrying unmanned aerial vehicle. The traveling mechanism comprises a dust cover, a grooved pulley and a cleaning plate, the grooved pulley and the cleaning plate are mounted in the dust cover and rotationally connected with the dust cover, and the traveling mechanism is movably connected with the equipment frame. The device has the advantages of being high in detection efficiency, good in stability and capable of achieving live detection, and solves the problems that in the prior art, in the power transmission line strain clamp detection process, power failure is needed for detection, normal life of nearby residents is affected, detection efficiency is low, and stability is poor.

The invention discloses an electronic component surface defect analysis system based on an attention mechanism, which comprises an image acquisition module, a data processing module, an attention mechanism module, a surface defect analysis module, a system maintenance and feedback updating module and a report generation and user interface module, the data processing module is used for preprocessing the collected image, the attention mechanism module is used for highlighting key area features in the image, the surface defect analysis module is used for detecting and analyzing device defects, and the system maintenance and feedback updating module is used for system maintenance and updating. And the report generation and user interface module is used for generating a detection report and providing an interactive interface. According to the electronic component surface defect analysis system based on the attention mechanism, a key defect concentration algorithm based on the attention mechanism is provided to extract key image defects; and providing a surface defect analysis algorithm based on Fourier transform to detect and analyze the surface defects of the device.

The invention relates to a malicious script detection method and device, equipment and a storage medium, and the scheme not only can generate a static analysis result through the analysis of a script text, but also can generate a dynamic analysis result according to an operation event of a target script in a running process. And a comprehensive risk assessment result is determined through a machine learning model and a heuristic rule set. Through the mode, malicious behaviors executed by the target script in the running process can be discovered, so that the scheme can effectively detect the novel malicious script; in addition, according to the scheme, the target script is operated through the sandbox container, and the detection efficiency of the malicious script can be improved.

The invention provides a power station photovoltaic module defect detection method and device based on unsupervised learning. The method comprises the steps that firstly, a thermal imaging image to be detected and a visible light image in a target area provided with a photovoltaic module are collected; cutting a thermal imaging photovoltaic module image from the thermal imaging image, and cutting a visible light photovoltaic module image from the visible light image; afterwards, a teacher network model and a visible light photovoltaic student network model in the photovoltaic defect detection model are used for processing the visible light photovoltaic module image, and position information of the visible light abnormal photovoltaic module is determined and returned; and the teacher network model and the thermal imaging photovoltaic student network model in the photovoltaic defect detection model are used to process the thermal imaging photovoltaic module image, and the position information of the thermal imaging abnormal photovoltaic module is determined and returned. Wherein the photovoltaic defect detection model is obtained by training in a label-free supervision mode.

The invention discloses a plant disease and insect pest detection device and method based on unmanned aerial vehicle high-altitude texture imaging, and belongs to the technical field of intelligent agriculture. The plant disease and insect pest detection device comprises an unmanned aerial vehicle body, a holder, a high-altitude texture camera and a small host; the high-altitude texture camera is arranged below the holder, the holder is arranged below the small host, the small host is arranged below the unmanned aerial vehicle main body, the unmanned aerial vehicle main body is provided with a front high-definition camera, a battery and an illumination sensor, and the high-altitude texture camera is wirelessly provided with a handle and a flight remote controller through Bluetooth; according to the plant disease and insect pest detection method, high-precision recognition of disease and insect pests is achieved on the basis of high-altitude texture feature fusion in combination with an environment perception driven data enhancement module, a self-adaptive multi-scale image convolution feature extraction and fusion module and a disease and insect pest area attention fusion module. The technology effectively supports precise detection of diseases and insect pests in agricultural production, and provides a scientific basis for agricultural management and pesticide application.

The invention discloses a steel plate butt weld pseudo defect ultrasonic detection method, system and device and a medium, and relates to the technical field of weld detection, the method comprises the following steps: setting a plurality of detection areas on a steel plate butt weld based on acquired structure parameters and surface characteristic data of a to-be-detected steel plate; performing multi-frequency and multi-angle ultrasonic scanning according to a preset first detection parameter by taking each detection area as a basic unit to obtain corresponding echo signal data; analyzing and identifying potential pseudo-defect signals, and calculating corresponding pseudo-defect characteristic values according to the pseudo-defect signals; taking each detection area as a basic unit, performing pseudo defect feature analysis according to the second detection parameters, and determining reliability evaluation data of pseudo defects; based on the pseudo-defect reliability evaluation data and the pseudo-defect characteristic value corresponding to each detection area, the pseudo-defect condition of the steel plate butt weld is analyzed, and a final weld quality comprehensive evaluation result is generated. According to the invention, the detection accuracy of steel plate butt weld detection is improved.

The invention relates to the technical field of detection kits, in particular to a detection kit for neutrophil gelatinase-associated lipocalin and a preparation method of the detection kit. The invention provides a detection kit for neutrophil gelatinase-associated lipocalin. The detection kit comprises an independently packaged reagent R1 and an independently packaged reagent R2, the reagent R1 comprises an HBR series specific blocking agent; the reagent R2 comprises carboxyl latex microspheres coupled with an NGAL antibody; the carboxyl content of the carboxyl latex microspheres is greater than 100 [mu] eq / g, and the particle size is 100-400 nm. According to the kit, an HBR series specific blocking agent is used as a component of the reagent R1, so that endogenous interference is reduced, and false positive and negative detection results of a sample are avoided. Furthermore, the latex with proper particle size is selected to be coupled with the polyclonal antibody, and the sealing condition is optimized, so that the method has the advantages of wide linearity, good analysis sensitivity, simplicity in operation, controllable batch difference and controllable cost.

The invention relates to the technical field of image processing, in particular to a bamboo shoot quality detection method and system based on a hyperspectral image and a medium, and the method comprises the following steps: acquiring a first hyperspectral image acquired from a target bamboo shoot; obtaining K characteristic wave bands of a target bamboo shoot, extracting K first spectral images in one-to-one correspondence with the K characteristic wave bands from the first hyperspectral image, and respectively determining reflectivity matrixes of the K first spectral images; determining the spectral deviation degree corresponding to the first spectral image based on the reflectivity matrix, and determining the quality of the target bamboo shoot based on the spectral deviation degree of each first spectral image; the method can realize lossless and efficient detection of bamboo shoot quality.

The invention discloses a pet peculiar smell detection method and device and a storage medium. The pet peculiar smell detection method comprises the following steps: collecting data in a to-be-detected environment in real time through a sensor array; performing temperature compensation on the sensing signal through a weighted linear temperature compensation algorithm and a weighted adaptive whale optimization algorithm; setting a threshold value, comparing the compensated sensor signal amplitude with the set threshold value, and when any sensor signal amplitude is triggered, starting to collect sensor data with a fixed length; inputting the collected sensor data with the fixed length into a self-adaptive time convolution GSA-TCN network model based on a gating convolution kernel rarefaction strategy for classification and identification; when it is judged that the pet excretes peculiar smell, table look-up, interpolation and concentration calculation are carried out according to the maximum value of the sensor data with the fixed length, and the detection result and the concentration value of the environment to be detected are output. The pet peculiar smell can be effectively detected, a series of problems caused by the pet peculiar smell can be solved, and the application scene is wide.

The invention discloses a remote sensing image target recognition method and system based on a visual language model, and relates to the technical field of image recognition. The method comprises the following steps: constructing an initial image detection model based on a real-time open vocabulary detector YOLO-World and a remote sensing visual language basic model RemoteCLIP; adding a double-attention fusion encoder and a multi-level text guide fusion decoder on the initial image detection model to obtain a target image detection model; performing knowledge self-adaptive adjustment and optimization on the basis of the pre-training weight of the target image detection model to obtain a final image target recognition model; and performing target identification on the remote sensing image based on the image target identification model. According to the method, a powerful visual language model is introduced, and an image feature extraction and target classification strategy is optimized, so that the accuracy and efficiency of target recognition in the remote sensing image are improved.

The invention relates to the technical field of image data processing, in particular to a power transmission line inspection method based on image processing, which comprises the following steps of: shooting an image of each conductor of a power transmission line, extracting a conductor area in the image, and clustering pixel points in any conductor area by using an improved mean shift algorithm to obtain a clustering result; a plurality of clustering clusters are obtained through an improved mean shift algorithm, and whether defects exist or not is judged according to the ratio of the number of pixel points in each cluster to the total number of pixel points in the wire area; when the ratio exceeds a set threshold value, judging that a defect exists in the lead area of the section corresponding to the pixel points in the clustering cluster; wherein the improved mean shift algorithm comprises the adjustment of bandwidth, so as to better adapt to the complexity and diversity of images. The problem that the accuracy is not high in the defect detection process of the power transmission line is solved.

The invention discloses an enhanced photoacoustic microscopic imaging method based on physical degradation learning, which comprises the following steps of: on the basis of a photoacoustic imaging principle, degrading an image of a real photoacoustic microscopic image to the ground into a low-quality photoacoustic microscopic image through physical model degradation learning with variability. A high-quality photoacoustic microscopic imaging image is used as a target of supervised training, deep learning is used as a basis, the high-quality photoacoustic microscopic imaging image is input into a self-designed convolutional neural network with an attention mechanism for network training, the quality of a reconstructed image is evaluated until the reconstruction quality reaches the standard, and otherwise, the above steps are executed again. And inputting a real low-quality photoacoustic image into the trained neural network to obtain a reconstructed high-resolution photoacoustic image. According to the method, the resolution ratio and the contrast ratio of the deep photoacoustic microscopic image can be effectively improved, meanwhile, the photoacoustic microscopic image can be denoised, the dependence on the data set can be effectively reduced due to the fact that the data set is obtained through the physical model, and the reconstruction effect under different environments can be improved.

The invention discloses a control system and method for multi-channel treatment of a biological sample. A capture antibody layer of an optical fiber SPR sensor and a real-time detection biological solution can be prepared; comprising a microfluidic module, a sensing module and a computer, the sensing module is used for monitoring resonance wavelength information of a biological solution flowing through a sensing area of the optical fiber SPR sensor in real time; the micro-fluidic module is used for enabling the solution in the corresponding liquid storage tank to flow through the outer surface of the optical fiber SPR sensor in the micro-fluidic chip through the micro-fluidic channel under the set pressure; and the computer is used for detecting the corresponding content of the biological solution in the current liquid storage tank by measuring the resonant wavelength and comparing the resonant wavelength with pre-stored data based on the received electric signal of the spectrum. According to the control system, a micro-fluidic chip is used as a container for accommodating a sample to be detected or prepared, and a biological solution slowly flows into the micro-fluidic chip through a micro-fluid channel so as to realize preparation of a capture antibody layer or real-time detection of the biological solution.

The invention relates to the technical field of weld joint detection, in particular to a CO2 welding automatic trolley weld joint recognition method. According to the method, by measuring current signals and magnetic conductivity parameters in real time and combining comparison of dynamic change amplitudes, the initial position of a weld joint is accurately recognized, the initial accuracy of weld joint detection is improved, meanwhile, a displacement characteristic curve is extracted, weld joint form data is dynamically adjusted, the continuity and accuracy of weld joint path recognition are ensured, and the path tracking effect is improved; the welding seam boundary and width range are accurately judged in combination with electromagnetic parameter differences, the reliability of welding seam boundary detection is facilitated, irrelevant parameters are removed through frequency selection, edge contours are captured, welding seam surface features are marked, feature point layered clustering and normalization processing are conducted through a Gaussian mixture model, welding seam feature point data are comprehensively integrated, and the accuracy of welding seam boundary detection is improved. The generated feature point model provides an important basis for high efficiency and accuracy of weld joint recognition, and the performance of weld joint detection in the aspects of paths, forms and boundaries is integrally optimized.

The invention belongs to the field of electrochemical biosensors, discloses a preparation method of an electrochemical biosensor capable of being used for multi-mode sweat detection, and particularly provides a single-electrode multifunctional arrangement method in a three-electrode system. A sensor material with high sensitivity and high selectivity is prepared, then the sensor material is printed through screen printing and assembled into a portable device to achieve simultaneous detection of multiple biomarkers in sweat, and the preparation method comprises the steps of selection and modification of an electrode material, sensor assembly and integration of a signal acquisition and analysis system. Through innovative material design and process optimization, the sensor not only can acquire information of various biomarkers in sweat in real time, but also can provide reliable data analysis, and shows excellent long-term stability and anti-interference capability in practical application. The technology provides an effective and reliable solution for real-time health monitoring.

The invention relates to an intelligent behavior analysis and anomaly detection system for Internet of Things security, which is applied to the technical field of Internet of Things security, and comprises a data acquisition module, a data preprocessing module, a behavior modeling module and an anomaly detection module, the data preprocessing module cleans the data, the behavior modeling module generates a vector sequence of equipment behaviors through feature embedding and position coding, and the anomaly detection module analyzes time sequence association of the equipment behaviors by utilizing a self-attention mechanism and a multi-head attention mechanism and judges whether the equipment has abnormal behaviors or not. The cooperative defense module realizes cross-device cooperative defense through an edge detection unit and a cloud analysis unit, and the threat intelligence sharing module realizes sharing and updating of threat intelligence through a local intelligence database and an external intelligence interface. And the response module carries out automatic defense and notifies a system administrator through the automatic defense unit and the alarm unit when an abnormity is detected.

The invention discloses a method for detecting internal body defects of a crystal. The method comprises the following steps: S1, taking the detected crystal with the internal body defects; s2, irradiating from the side surface of the crystal by using a line light source so as to obtain a crystal tomographic image containing scattered light of body defects in the crystal; correcting the size of the crystal chromatography image; s3, performing time sequence analysis and processing on the corrected crystal chromatography image, and highlighting the characteristics of defect points; s4, in combination with the internal defect position of the known crystal and the detection time sequence signal of the position, training the RNN model to obtain a crystal internal defect detection model; s5, repeatedly training to obtain a final crystal internal defect detection model; and S6, inputting the time sequence signal of the crystal to be detected, which is obtained by processing in the steps S2 to S3, into the final model, so as to obtain the position information of the internal defect. And S7, filtering out false defects according to the representation of the defects on the plane. The method solves the problems of complex identification process and low identification efficiency of the internal defect of the crystal at present.

The invention belongs to the technical field of magnetic flux leakage image processing, and discloses a pipeline magnetic flux leakage image detection method based on a multi-scale SegFormer network, and the method comprises the steps: S1, obtaining a magnetic flux leakage signal in a pipeline through a magnetic flux leakage detector; s2, carrying out image enhancement by adopting a pseudo-color processing technology, and carrying out pseudo-color enhancement by adopting density segmentation; s3, a multi-scale feature fusion and cascade attention module is added into the SegFormer network, and a new SegFormer network model is obtained; and S4, carrying out magnetic flux leakage image segmentation through the improved SegFormer network. The method is scientific and reasonable in design, multi-scale feature fusion is used for extracting richer low-level and high-level semantic information features of a small target, and the cascade attention module enhances the perception and representation ability of the model to local features, so that the expression of the model in magnetic flux leakage defect segmentation is improved, the segmentation precision of the magnetic flux leakage defect can be effectively improved, and the segmentation efficiency of the magnetic flux leakage defect is improved. The method has a certain guiding effect on efficient and accurate detection of magnetic flux leakage signals.

The invention discloses a heat supply pipe network line anomaly judgment method based on temperature and pressure detection, and relates to the technical field of pipeline anomaly detection, and the method comprises the following steps: S1, scanning a two-dimensional code of a to-be-detected point, and reading equipment information of the to-be-detected point; s2, reading real-time temperature data and real-time pressure data measured by a built-in sensor of a point to be detected; s3, analyzing a reasonable temperature difference value between the pipeline branch where the to-be-detected point is located and the heat source by acquiring environmental data of the to-be-detected point; s4, analyzing a reasonable pressure difference value between the pipeline branch where the to-be-detected point is located and the heat source by acquiring terrain data of the to-be-detected point; and S5, according to the detected points in the distribution diagram of the heat supply network, verifying the abnormity judgment result of the current pipeline branch, and after the verification is passed, marking the abnormity information of the pipeline branch. Real-time temperature and pressure data are collected, and dynamic analysis is performed according to a set sampling period, so that the real-time performance and accuracy of a monitoring result are ensured.

The invention relates to the technical field of computer vision and deep learning, and relates to a deep forgery detection method based on mask supervision. According to the technical scheme, an image is input to a preprocessing module for preprocessing; the processed image is respectively input into a mask supervision module for extracting spatial domain features and a frequency domain feature extraction module for extracting frequency domain features; and the spatial domain features and the frequency domain features are connected and input to a classification module to obtain a final prediction result. The method has the beneficial effects that the attention of the model to the irrelevant region is supervised and corrected through the mask, and a pixel-level label is used for guiding the model to face the synthesis region of the face, so that more accurate extraction of spatial features is ensured; the depth forgery detection method based on mask supervision has remarkable advantages in the aspects of detection accuracy, robustness, generalization ability, calculation efficiency, interpretability, overall performance optimization and the like, and a new solution is provided for the field of depth forgery detection.

The invention discloses a fault diagnosis method for a turbine pump bearing of a liquid rocket engine based on dual adaptive filtering and related equipment, and the method comprises the steps: processing an original vibration signal through an adaptive line spectrum intensifier, and obtaining a vibration signal after the interference of a first-order cyclostationary signal is eliminated; blind cyclic frequency detection is carried out on the vibration signals after the first-order cyclostationary signal interference is eliminated, and a cyclic frequency set is obtained; taking the vibration signal after eliminating the interference of the first-order cyclostationary signal as the input of a multi-channel adaptive FRESH filter, taking the frequency shift component used by each channel as the frequency in a cyclic frequency set, and outputting the filtered vibration signal; and analyzing the filtered vibration signal by using cyclostationary analysis to obtain a diagnosis result. According to the method, a pure second-order cyclostationary signal related to a fault can be accurately extracted from a fault liquid rocket engine turbine pump bearing vibration signal, the filtering process can adapt to the change of a system state, and a reliable pretreatment result can be provided for a bearing fault diagnosis method based on cyclostationary analysis; and finally, accurate diagnosis of the turbine pump bearing fault of the liquid-propellant rocket engine is realized.

The invention relates to the technical field of medical detection, and discloses a capsule endoscope image auxiliary diagnosis method based on deep learning, which comprises the following steps: acquiring an original image sequence of an alimentary canal shot by a wireless capsule endoscope, screening out an image sequence related to the alimentary canal in the original image sequence, and labeling the image sequence; sequentially performing data enhancement, geometric transformation and standardization processing on the marked image sequence, and clustering a prior anchor frame by using a K-means algorithm to form a capsule endoscope image data set; a deep learning model LD-YOLO is built, a YOLOv5 model is selected as a basis, optimization is carried out on the basis of the basic model, and a capsule endoscope image auxiliary diagnosis model is built; and training a capsule endoscope image auxiliary diagnosis model through the capsule endoscope image data set, and detecting an image to be diagnosed by using the trained capsule endoscope image auxiliary diagnosis model. According to the invention, the workload of doctors in image analysis can be greatly reduced, and the film reading efficiency is improved.

The invention belongs to the field of non-intrusive load monitoring, and discloses a household electricity consumption analysis method based on non-intrusive load monitoring and related equipment, firstly, non-intrusive monitoring is realized by using household total electricity meter data instead of a single equipment electricity meter, and the complexity and cost of household electricity consumption data collection are reduced; secondly, an unsupervised learning method is adopted to cluster the power load state data, data does not need to be marked in advance, and different load events are automatically identified and classified, so that the problems of load type diversity and high identification difficulty are effectively solved, and the accuracy and efficiency of load event detection are improved; in combination with a depth-first search algorithm and load event constraint conditions, fine search is carried out on load event labels, an undetermined event sequence set is generated, the logicality and continuity of event sequences are enhanced, electricity utilization behavior analysis of the electric equipment is deeper and finer, home users can better understand and optimize own electricity utilization modes, and the user experience is improved. And energy conservation, emission reduction and efficient utilization of energy are promoted.