177results about How to "Implement classification" patented technology

The invention discloses an intelligent software testing method and system based on automatic script generation, and relates to the technical field of software testing, and the method comprises the following steps: deploying a distributed data acquisition probe, dynamically adjusting sampling frequency, collecting software operation data, and extracting data features based on a deep learning model; based on the data features, establishing a behavior pattern classification system, and mapping behavior patterns to realize hierarchical behavior classification; the method comprises the steps of constructing a test scene, generating test data, executing a test task, monitoring a test execution state in real time, calculating a test efficiency index, evaluating and feeding back a test effect, and dynamically adjusting a feature extraction strategy. According to the method, deep learning and reinforcement learning technologies are utilized, accurate recognition and classification of software behavior modes are achieved, and comprehensiveness and effectiveness of testing are guaranteed through multi-dimensional coverage rate analysis and a closed-loop optimization mechanism; and meanwhile, the automation level and the intelligent degree of software testing are remarkably improved, and a powerful guarantee is provided for improving the software quality.

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention provides an AI-based pipe gallery fire operation hidden danger automatic identification method and device. The method comprises the steps of collecting data of a pipe gallery fire operation site in real time and preprocessing the data; identifying and comparing the check points by adopting an image detection classification algorithm based on a diffusion model and a large model distillation technology to obtain an identification and comparison result; and calling a pre-constructed LLM large language model, carrying out potential safety hazard identification on the preprocessed multi-modal data and the identification comparison result through a multi-modal input algorithm, and outputting a multi-modal fire operation comprehensive supervision report. A diffusion model and a large model distillation technology are utilized, data augmentation and data preprocessing are provided, image detection and classification of a pipe gallery fire operation site are realized, and real-time monitoring, automatic hidden danger identification and intelligent early warning of the pipe gallery fire operation site can be realized through LLM construction of an overall system framework and multi-modal input algorithm analysis. And the accuracy and efficiency of potential safety hazard identification are improved.

The patent of the present invention discloses an automatic classification and recycling device for kitchen waste, which specifically relates to the technical field of resource recycling. An automatic classification and recycling device for kitchen waste, including a recycling box, a stepper motor is provided on the recycling box, and a first rotating shaft is connected to the stepper motor; a holding plate is distributed on the first rotating shaft, and a first gravity sensor is provided on the first rotating shaft; a first fixed ring is provided in the recycling box, a threaded rod is provided in the first fixed ring, a clamping plate is connected to the threaded rod, and a second gravity sensor is also provided on the clamping plate; a first driven gear is provided on the threaded rod; a second rotating shaft and a partition plate are provided in the recycling box, a first motor is provided on the second rotating shaft, a blade is provided on the second rotating shaft, and a second motor is also provided on the recycling box, and a driving gear is provided on the second motor, and the driving gear is meshed with two first driven gears. The technical solution of the present invention solves the problem that the existing office building garbage cannot be classified and recycled, and can be used for the centralized recycling of kitchen waste.

The invention discloses an event detection and extraction method and system based on entity and trigger word fusion recognition. The method comprises the steps that trigger words in event text data are extracted; mapping the event text to a vector space based on a word embedding model, and constructing an event text classifier to perform similarity measurement on all words of which the part-of-speech is a verb and the trigger word in the event text; constructing an event detection model to obtain an event category in the event text data; identifying a key element entity of the event; constructing a dependency syntax tree to represent a syntactic dependency relationship of the sentences; obtaining the score of the dependency syntax tree; and inputting the preprocessed word vector and the part-of-speech vector representation obtained through supervised learning into a role tagging model for fusion to obtain a vector representation with part-of-speech features, and after training, carrying out conditional random field processing to obtain a final semantic role tag of the word. Accurate event detection based on entities is realized, and high-quality extraction of events is towed.

The invention relates to the technical field of industrial intelligent manufacturing, in particular to a flexible production line appearance defect optical detection method. Comprising the steps of performing image acquisition on semi-finished products and finished products on a production line; carrying out preprocessing and spatial transformation on the collected image; performing feature extraction and clustering analysis on the positive sample image of the to-be-detected product by using unsupervised learning to form a feature library; comparing the feature vector of the to-be-detected image with a feature library, and outputting the defect position and type of the sample; an improved RTDETR model with supervised learning is adopted, a sample judged as a defect is used for training, and the improved RTDETR model adopts a ResNet and Visual Transform network structure; and performing real-time detection and result feedback on the deployment model. According to the technical scheme, the appearance defect detection efficiency and the product quality of products manufactured on the flexible production line can be improved.

The invention discloses a typical adjustable resource optimization method and system for a power grid adjustment scene, and the method comprises the steps: carrying out the step extraction of operation data for the historical operation data of different adjustable resources; setting typical scene response characteristics by considering response characteristic differences of the adjustable resources in different power grid adjustment scenes, constructing an adjustable resource response potential evaluation index system oriented to the power grid adjustment scenes, and calculating various evaluation indexes; the matching degree difference between the multi-type adjustable resource response characteristics and the power grid adjustment demand is considered, an adjustable resource comprehensive evaluation model is constructed, and adjustable resource response potential comprehensive evaluation in a specific scene is achieved; in combination with an adjustable resource response feature combination and a comprehensive evaluation model, rapid grouping and optimization are carried out on adjustable resources by adopting a combination evaluation and affinity propagation algorithm, and classification and optimization of adjustable resource groups with different response potentials are realized; and in combination with classification and optimization results, analyzing user demand response potential, and optimizing energy management and demand response strategies.

The invention discloses a complex pavement damage condition detection method integrated with an unmanned aerial vehicle. The method specifically comprises the following steps: S1, an unmanned aerial vehicle platform and a sensor system; s2, image preprocessing and feature extraction; the invention relates to the technical field of intelligent traffic. According to the complex road surface damage condition detection method and device fused with the unmanned aerial vehicle, the unmanned aerial vehicle is high in inspection speed and wide in coverage range, the detection period is greatly shortened, the detection cost is reduced, and artificial intelligence technologies such as deep learning and machine learning are combined, so that accurate recognition and classification of road surface damage conditions are realized, and the detection precision is improved; a comprehensive detection report and three-dimensional visual analysis are provided, a scientific basis is provided for road maintenance decision, traffic safety management and urban planning, road maintenance is promoted to develop towards the intelligent and precise direction, the road maintenance efficiency and quality are improved, traffic safety is guaranteed, an interactive decision support system is developed, and the development of the road maintenance system is facilitated. And the user operation convenience and the decision-making efficiency are improved.

The invention discloses a GIS switch foreign matter detection method based on multispectral image fusion and related equipment, and belongs to the technical field of electrical equipment foreign matter detection, and the method comprises the steps: obtaining a multispectral image of a to-be-detected GIS switch; and inputting the multispectral image into a pre-trained multispectral fusion model to obtain a multispectral feature map, and inputting the multispectral feature map into a pre-trained multispectral image foreign matter detection model to obtain a foreign matter detection result of the to-be-detected GIS switch. The development and automatic identification of the tiny foreign matter material under different light wavelengths are realized by researching the development technology of the tiny foreign matter material under different light wavelengths and combining multispectral fusion and a multispectral image foreign matter detection algorithm, and the accuracy and robustness of detection are improved.

The invention discloses a hydroelectric generating set excitation power unit fault diagnosis method based on Attention-CNN and a fault knowledge base, and the method comprises the steps: combining normal data and fault simulation data, associating with a corresponding type code, and constructing a single-bridge excitation power unit fault knowledge base; dividing waveform data in the knowledge base into a training set and a test set according to a preset proportion, and inputting the waveform of the excitation voltage and the waveform of the AC side voltage as two-dimensional vectors into an Attention-CNN model for training and testing; and excitation voltage and alternating current side voltage waveforms during the operation period of the excitation system are collected in real time, the excitation voltage and alternating current side voltage waveforms are converted into two-dimensional vectors, the two-dimensional vectors are input into the trained Attention-CNN model, and type codes are output. According to the method, the fault diagnosis efficiency and accuracy of the single-bridge excitation system of the small-sized hydroelectric generating set are improved by using a deep learning technology, automatic identification of a complex fault mode is realized, and the operation stability and the fault processing capability of the small-sized hydroelectric generating set are improved.

The invention provides a battery cell tab cutting and inspecting machine, and relates to the technical field of battery cell tab cutting and inspecting machines, the battery cell tab cutting and inspecting machine comprises a transfer device, a turntable device, a cutting device, a shaping device, a CCD detection device, an OCV / IR detection device and a feeding device, the transfer device comprises a feeding linear module supporting seat, a first bottom plate, a discharging conveying supporting seat and a discharging conveying assembly, the two sides of the surface of the first bottom plate are fixedly connected with a discharging conveying supporting seat and a feeding linear module supporting seat correspondingly, a discharging conveying assembly is installed on the surface of the upper end of the discharging conveying supporting seat, and a discharging linear module is arranged on one side of the discharging conveying assembly. According to the invention, the problem that the battery cell is inconvenient to cut due to the fact that the position of the battery cell is easy to deviate in the cutting process of the battery cell tab and the welding process of the protection plate when the battery cell tab needs to be cut and the protection plate needs to be welded on the battery cell in the packaging process of the battery cell is solved.

The invention provides a cellular lung disease condition prediction method based on multi-modal time sequence data, and belongs to the technical field of cellular lung disease condition prediction. The technical problem to be solved is to provide a cellular lung disease condition prediction method based on multi-modal time sequence data. According to the technical scheme, the method comprises the steps that CT description texts and CT image data of a patient at different time points are collected, then the collected data are cleaned and labeled, and a comprehensive time sequence data set is constructed in combination with the data and used for training and testing a model; a time sequence model is built, the time sequence model comprises an input layer, a wavelet transform layer, a Transform layer, a fusion layer and an output layer, a built time sequence data set is input into the input layer, time sequence data is converted into embedded representation, and the input layer processes multi-modal data and prepares to be input into a subsequent layer; the method is used for predicting the condition of the honeycomb lung disease.

The invention provides a method for identifying and classifying reaction products of an alkali-activated fly ash and slag gelling system. The method comprises the following steps: acquiring a BSE image and an EDS image of an alkali-activated fly ash and slag gelling system sample; clustering based on the pixel color features of the EDS image to obtain each phase mask; fusing the phase feature of the BSE image into the EDS image, and extracting a hole structure based on the gray feature of the BSE image in the same view field area as the EDS image; according to the phase masks and the pore structures, obtaining an unreacted precursor phase mask from which the pore masks are deducted and a reaction product phase mask from which the pore masks are deducted; performing superpixel segmentation on the reaction product phase mask to obtain an Mg / Al ratio of each superpixel; and carrying out clustering analysis on the Mg / Al ratio of each superpixel to realize identification and classification of slag reaction products, mixed reaction products and fly ash reaction products. According to the method, refined classification of the reaction products in the fly ash slag alkali-activated gel system can be achieved, and the accuracy of recognition and classification of the reaction products is improved.

The invention discloses a glaucoma fundus image classification method, which comprises the steps of acquiring existing glaucoma fundus image data information, and performing classification labeling and preprocessing to obtain a training data set; constructing a glaucoma fundus image classification initial model based on a ConvNeXt model, and training to obtain a glaucoma fundus image classification model; and adopting the obtained glaucoma fundus image classification model to classify actual glaucoma fundus images. The invention further discloses an imaging method comprising the glaucoma fundus image classification method. According to the method, the glaucoma fundus image classification network with the feature fusion process is constructed through the ConvNeXt model, so that classification of the glaucoma fundus images can be achieved, the method is suitable for classification of monocular images and binocular images, reliability is high, accuracy is good, the application range is wide, and the method is more objective and scientific.

The invention belongs to the technical field of image processing, and particularly relates to a fresh meat quality evaluation method and system based on multi-modal learning, and the method comprises the steps: obtaining a fresh meat image; extracting meat texture density features of the image; performing multi-level grid division on the fresh meat image based on the meat texture density feature to obtain a texture density feature vector and a color feature vector; taking each grid region after grid division as a node, taking the obtained texture density feature vector and color feature vector as node features, and constructing a multi-scale graph neural network comprising a texture feature network and a color feature network; and performing multi-modal learning feature fusion on the node features according to the multi-scale graph neural network to obtain a texture density comprehensive feature vector and a color comprehensive feature vector, calculating the comprehensive feature vector, evaluating the texture complexity and color distribution characteristics of the meat quality region, and completing the quality evaluation of the fresh meat.

The invention discloses a heterogeneous graph convolution epilepsy classification model construction method, an epilepsy monitoring device, equipment and a medium, and relates to the field of intelligent medical instruments. The method comprises the steps of firstly constructing a feature extraction module; then constructing an adaptive graph convolutional network model fusing the face and the motion, namely an F2AGCN network model; and training an F2AGCN network model by using a modal coordination learning mechanism to obtain a trained F2AGCN network model, and taking the trained F2AGCN network model as a heterogeneous graph convolution epilepsy classification model. According to the method, the adaptive graph convolutional network model fusing the face and motion information is constructed and used for epilepsy classification, a modal coordination learning mechanism is introduced to force model learning to keep feature representation of semantic consistency between skeleton and face modals, it is ensured that fused features keep semantic consistency between different modals, and the classification efficiency of epilepsy classification is improved. Therefore, integration effectiveness is improved, and the constructed heterogeneous graph convolution epilepsy classification model can directly realize epilepsy classification and monitoring based on video data.

The embodiment of the invention provides an anomaly detection method and system for an air conditioning unit, electronic equipment and a storage medium, and belongs to the field of artificial intelligence and the field of predictive maintenance of industrial equipment. The method comprises the steps that operation parameters of the air conditioning unit are collected and input into an anomaly detection model, anomaly time sequence parameters in the operation parameters of the air conditioning unit are output, and the anomaly detection model is constructed based on a TimesNet algorithm; and inputting the abnormal time sequence parameters into a fault classifier to obtain a fault classification result of the air conditioning unit. A time sequence model of normal operation of the unit is established by using a Times Net algorithm, and multi-sensor operation parameters of the air conditioning unit are detected in real time. And inputting the time sequence interval in which the problem is found into a fault classifier which is jointly constructed based on a mode matcher and a multi-classifier to classify the type of the fault. Abnormality detection and classification of the air conditioning unit equipment are achieved, and the equipment operation efficiency is effectively improved.

The invention discloses a building pipeline intelligent flaw detection and leakage positioning system, which relates to the technical field of building pipeline detection and comprises a data acquisition module, a multi-modal fusion module, a defect identification module, a leakage positioning module, a digital twinborn module, a health prediction module and a decision control module. The data acquisition module is used for deploying multiple types of sensors along a building pipeline, acquiring acoustic emission signals, infrared thermal imaging images, pressure fluctuation data and fluxgate detection signals in a running state of the pipeline, and outputting original sensing data; the multi-modal fusion module is connected with the data acquisition module and is used for performing feature extraction on the original sensing data, constructing a multi-modal feature vector, dynamically adjusting each modal weight based on environmental parameters and outputting a weighted fusion feature vector; and the defect identification module is connected with the multi-modal fusion module and is used for inputting the weighted fusion feature vector into a deep neural network model.

The invention relates to the technical field of network security, in particular to a method, a system and equipment for processing virus characteristic data. The method comprises the following steps: configuring a virtual sandbox virtualization layer, initializing a running environment, setting monitoring parameters, verifying environment isolation and a running state, and generating virtual sandbox running data; acquiring virus sample data, and loading the virus sample data to the virtual sandbox to obtain sample initial state data; according to the sample initial state data and the virtual sandbox operation state data, virtual sandbox operation parameters are configured, a monitoring probe is deployed, and virtual sandbox probe deployment data are obtained; a behavior acquisition channel is established based on virtual sandbox probe deployment data, and a behavior data acquisition model is constructed. According to the method, the virus sample behavior analysis is carried out by constructing the virtual sandbox environment, the virus feature recognition process is optimized by combining feature extraction, dimension reduction and clustering analysis, and the virus detection efficiency, accuracy and real-time response capability are improved.

The invention provides a carbonate rock lithology identification method based on machine learning, and the method comprises the steps: obtaining the hyperspectral data of a carbonate rock sample through an ASD spectrometer, and carrying out the preprocessing of the hyperspectral data; a mixed feature selection method is adopted, firstly, feature wavebands in the hyperspectral data are selected, then a random forest method is adopted for feature importance sorting, and features with high importance are selected; constructing a carbonate rock lithology identification model based on the selected features, wherein model parameters are determined by a machine learning algorithm; and predicting the mineral content in the carbonate rock according to the lithology identification model so as to identify the lithology of the carbonate rock. According to the method, on the basis of carbonate rock hyperspectral data, feature selection is carried out by adopting a mixed feature selection method after preprocessing, a lithology identification model is established on the basis, model parameters are determined through a machine learning mode, mineral component content is predicted, and therefore carbonate rock lithology identification and classification are achieved. And the identification precision and efficiency are greatly improved.

The utility model relates to the technical field of crushers, and provides a raw material crusher for producing furfural, which comprises a shell, a feed port is arranged at the top of the shell, a first discharge port is arranged at the bottom of the shell, mounting grooves are arranged on two sides of the feed port, dustproof mechanisms for dust prevention are arranged in the mounting grooves, and the first discharge port is arranged at the bottom of the shell. A smashing cavity, a smashing cavity and a filtering cavity are sequentially formed in the shell, a plurality of smashing mechanisms used for smashing raw materials are arranged in the smashing cavity, a smashing mechanism used for smashing the raw materials is arranged in the smashing cavity, a filtering mechanism used for filtering the raw materials is arranged in the filtering cavity, and a transmission mechanism is arranged on the side face of the shell. The crushing mechanism and the plurality of smashing mechanisms are all linked with the transmission mechanism, each smashing mechanism is linked with the rotating motor, and by means of the technical scheme, the problems that in the related technology, the complexity of a system is increased, resource waste is caused, and the stability and quality of a final product are affected are solved.

The invention discloses a power transmission line lightning classification method, which comprises the following steps: acquiring an original image, and cutting by using a lightning target cutting method to obtain a lightning target image; selecting different background images in the original image, splicing the lightning target image with the different background images, and performing first preprocessing on the spliced image to obtain a background image; modeling the background image by using a Gaussian mixture model to obtain a background model, and calculating the matching degree between each pixel of the background model and each Gaussian model distribution to obtain a foreground; judging the foreground through a threshold cutting method to obtain a lightning region, and performing second preprocessing on the lightning region to obtain a first lightning region; and inputting the first lightning region into a deep learning model for training and classification reasoning to obtain a confidence score, and marking the lightning type and the hazard level according to the confidence score to obtain a lightning classification result, thereby meeting the requirements of power transmission line monitoring safety guarantee.

The invention relates to the technical field of garbage recycling, and provides a vehicle-mounted garbage recycling comprehensive utilization station which comprises a winnowing device arranged on a vehicle body and used for dividing garbage into light garbage and heavy garbage. The electrostatic separation device is arranged on the vehicle body and is used for screening light garbage; the magnetic separation device is arranged on the vehicle body and is used for screening metal in the heavy garbage; the negative pressure screening device is arranged on the vehicle body and used for screening glass in the heavy garbage, the magnetic separation device is located above the negative pressure screening device, and the electrostatic separation device and the crushing device are located on one side of the negative pressure screening device. By means of the technical scheme, the problem that in the prior art, a vehicle-mounted garbage comprehensive recycling station cannot conduct garbage classification is solved.

The invention discloses an urban tunnel limit invasion prevention intelligent early warning method based on distributed acoustic sensing, and relates to the field of underground monitoring and rail safety early warning, and the method specifically comprises the steps: inputting a data record of a distributed optical fiber; the records are subjected to standard size windowing, spectral amplitude solving, range intercepting, data truncation and normalization; training a convolutional auto-encoder to reconstruct data, and performing encoding dimension reduction on the data by using an encoder part; training a random forest classifier by using the feature vectors subjected to real-time data preprocessing and dimension reduction; continuous data is preprocessed, and after dimension reduction of the data by a convolution encoder, detection and classification are carried out by using a random forest; and performing post-processing based on signal space-time continuity screening on a detection result to obtain an accurate detection and classification result. According to the invention, the construction signals in the distributed acoustic sensing data are monitored in real time by using the convolution auto-encoder and the random forest algorithm, detection and accurate classification are carried out with high temporal-spatial resolution, and intelligent early warning of track construction based on distributed optical fibers is realized.

The invention discloses a seal identification method and device, equipment, a medium and a product, which are applied to the field of financial science and technology, and the method comprises the steps: obtaining a to-be-identified image, and determining a target prediction box in the to-be-identified image through a pre-constructed target detection model, the to-be-identified image comprising an unformatted contract; and determining a target feature category of the to-be-recognized image according to the target prediction box and a pre-constructed feature recognition model, and generating a seal recognition result of the to-be-recognized image according to the target feature category. The advanced single-shot multi-frame detector SSD network technology is utilized to adapt to various complex seal styles and image backgrounds, the generalization ability of the model is improved, the target area in the to-be-recognized image can be detected through the target detection model, and the seal positioning accuracy and speed are improved. And the generated seal identification result provides valuable information for unformatted contract management of a bank, so that automatic filing, classification and statistics are realized, and the management efficiency and precision are improved.

The invention relates to the field of planetary gear box fault diagnosis, and discloses a planetary gear box fault diagnosis method, system, medium and equipment based on visual atlas feature fusion, and the method comprises the steps: collecting a normal state and a fault time domain signal during the operation of a planetary gear box through a pre-built planetary gear box working platform; carrying out Welch transformation on the time domain signal to obtain a power spectrum, and constructing a visibility graph map by adopting a visibility graph algorithm according to the power spectrum so as to extract centrality indexes of map nodes and fuse the centrality indexes into a feature matrix; the feature data is normalized and divided into a training set and a test set, a CNN-Inception model is trained based on the training set, the CNN-Inception model is checked by the test set, and the performance index is the accuracy rate of fault diagnosis; and after feature extraction is carried out on planetary gearbox signals collected in real time, the planetary gearbox signals are input into the trained CNN-Inception model for diagnosis. Effective feature information can be extracted, and the model diagnosis accuracy is improved.

The invention provides an automatic garbage sorting method, device and equipment for a household garbage robot, and relates to the technical field of robots, the method comprises the following steps: combining the position of the robot, the position of each garbage and the throwing position of each garbage to formulate an initial driving route of the robot; obtaining the remaining garbage storage space of the robot and the garbage characteristics of the garbage; according to the remaining garbage storage space of the robot and the garbage characteristics of the garbage, the initial driving route is adjusted, and a target driving route of the robot is generated; and dynamic obstacle information in the target area is obtained, the target driving route is adjusted according to the dynamic obstacle information, and a final driving route of the robot is generated, so that the robot processes the garbage in the target area according to the final driving route. The garbage collection device has the technical effects that garbage scattered everywhere is automatically collected, and comprehensive classification and collection of the garbage are achieved.

The present invention discloses a fine-grained three-dimensional model classification method based on deep integration and detail perception. The method realizes fine-grained classification of three-dimensional model multi-views based on a three-dimensional model fine-classification network. The three-dimensional model fine-classification network consists of a backbone network and an auxiliary network. The backbone network consists of an initial feature extraction layer, a group of base learners and a deep integration learner, which is used to learn the global structural features of the three-dimensional model contained in the multi-views. The auxiliary network consists of a context detail perception module, a view maximum pooling layer and a fully connected layer, which is used to capture the detail features contained in the view; the classification decisions of the backbone network and the auxiliary network are combined to form a comprehensive classification decision of the overall network, and a joint loss function is constructed based on this, so that the three-dimensional model fine-classification network can accurately classify fine-grained three-dimensional models. The present invention can effectively capture fine-grained discriminative information and has good classification effect and universality in three data sets.

The application discloses a cigarette case intaglio printing surface quality evaluation method and system based on image features, and relates to the fields of cigarette case intaglio printing quality detection and machine vision technology; the application collects cigarette case intaglio printing images through an image acquisition unit and pre-processes the images into gray-scale images, extracts multi-scale gradient features to obtain a comprehensive feature index for preliminary defect determination, analyzes edge morphology and texture periodicity in a transition area to obtain a comprehensive defect discrimination index, obtains a defect score based on the multi-scale comprehensive feature index and the comprehensive defect discrimination index, and completes defect classification and positioning through spatial clustering and secondary verification; four quality grades are divided based on defect proportions and defect numbers, a defect labeling map, a heat map and a standardized quality evaluation report are generated, and the problem that extracted image features cannot effectively distinguish normal dots from defective dots and result in a high false detection rate is effectively solved, and the precision and efficiency of cigarette case intaglio printing surface defect detection are significantly improved.

The application discloses an expert warehousing method and system for petrochemical engineering. The experts to be warehoused are composed of application warehousing experts and designated warehousing experts with high-level professional technology. The method comprises the following steps: collecting basic information and work experience information of historical warehousing experts, analyzing the influence of the operation characteristics of the units to which the experts belong on the experience and professional skills of the experts, and determining multiple expert information classification items as expert extraction conditions; determining whether each application warehousing expert has the qualification of bid evaluation according to the sub-information under each classification item in the reported expert information of each application warehousing expert, then warehousing the experts with the qualification of bid evaluation and the sub-information under each classification item as the warehousing objects to form an initial expert warehouse; and updating the current initial expert warehouse by using the sub-information under the specific classification items configured for each designated warehousing expert, so as to complete the warehousing of experts. The application realizes the warehousing and classification of petrochemical engineering experts.