208 results about "Material classification" patented technology

The invention relates to the technical field of machine vision detection, and discloses a mobile phone shell production quality detection method and device based on machine vision. The method comprises the following steps: carrying out original multi-physical field data acquisition and space-time registration preprocessing on a mobile phone shell to be detected to obtain multi-modal standardized data; then, through surface texture region segmentation and edge contour extraction, correlation mapping of material classification and defect types is achieved, a mobile phone shell multi-modal feature vector is obtained, functional region differentiation feature fusion and injection molding process correlation reconstruction are conducted on the multi-modal feature vector, lightweight feature representation is obtained, and the mobile phone shell defect detection method is achieved. Generating an initial defect detection result through multi-class defect identification and quality grade classification; and performing defect visual feature extraction and feature comparison based on a historical qualified sample feature library to obtain defect deviation features, and generating a final quality detection result of the mobile phone shell to be detected through visual quality grade mapping. The accuracy and the process adaptability of mobile phone shell production quality detection are improved.

The invention relates to the technical field of computer vision, in particular to a station platform limit intelligent detection method and system based on computer vision, and the method comprises the following steps: obtaining platform region spectral data, extracting spectral features according to the reflectivity value of each pixel point in a spectral range, comparing the spectral features with the features of various known materials, and determining the position of the station platform limit. And identifying material types of the plurality of positions, detecting obstacles, and generating material classification feature values. According to the method, the spectral data is acquired, the reflectivity value is compared to identify the material type, the texture feature and the gradient change rate are utilized to analyze and identify the fuzzy area, misjudgment on the fuzzy identification is reduced, point cloud data and image information are adopted for time-space synchronous registration, and multi-source sensing information is fused to enhance the time-space consistency of three-dimensional reconstruction. The size accuracy of the platform model is improved, the space offset is calculated by comparing engineering design drawing data, a structure deviation quantitative evaluation system is established, a deformation area is positioned, and the foreseeability of safety early warning is improved.

When x-ray fluorescence (“XRF”) spectroscopy is utilized to classify materials transported on a moving conveyor belt, there is the possibility of the x-ray beam only partially irradiating the material piece, which can result in the capture of an inaccurate XRF spectrum needed to classify the material piece. This can lead to an improper (erroneous) classification and resultant sortation of material pieces (e.g., aluminum alloys). In a material handling system, the area of the intersections between the x-ray beam spots from an x-ray fluorescence system and the material pieces are measured and correspondingly used to correct the measured XRF spectrum associated with each material piece. The material pieces can then be sorted according to the corrected XRF spectrum.

The invention relates to a railway material purchasing asynchronous task processing method, device and system, and the method comprises the steps: constructing a six-layer message channel architecture of a purchasing business process, and executing an instruction of deploying a message agent node in an active-active mode for each channel based on the core message middleware of a Kafka cluster; the front-end gateway is instructed to package the purchase event into a binary message body with a transaction ID and a timestamp, and the binary message body is written into a persistent queue after priority labeling is carried out according to a task type label; processing the concurrent tasks by adopting a multi-stage feedback queue scheduling algorithm, and dynamically allocating computing resources based on a thread pool; a maximum retry threshold value is set according to an exponential backoff retry strategy, so that the failed task is automatically transferred into a dead message queue; and dynamically calculating an optimal message routing path based on the supplier portrait and the material classification model, and extracting the order to an exclusive processing queue of the optimal supplier according to Topic dynamic switching of the message queue. According to the invention, the coupling degree is reduced, the system performance is improved, and the fault tolerance is enhanced.

The invention provides an imaging method and system based on a polarization imaging lens and visual collaborative optimization, and a medium, and the method comprises the steps: integrating a four-way polarization filter array on the surface of an imaging sensor, and synchronously collecting a four-channel polarized light intensity image through single-frame exposure based on the imaging sensor; obtaining a three-channel RGB image, and inputting the four-channel polarized light intensity image and the three-channel RGB image into a polarization-RGB fusion neural network; polarization features and RGB features are extracted, heterogeneous information coupling optimization is carried out, and fusion features are obtained; constructing a polarization-color joint decoding network, carrying out polarization and RGB information collaborative decoding on the fusion features, training a polarization decoding process and a material classification task together based on an end-to-end joint optimization strategy, and outputting a material classification result; high-efficiency acquisition of original polarization information on a hardware level is guaranteed through the polarization filtering array; and an end-to-end joint training strategy is adopted to realize optimal decoupling and fusion of perception information.

The invention provides a bottle sorting and data monitoring method and system and bottle recycling equipment. The method comprises the following steps: collecting a visible light image and near infrared spectrum data of a bottle target, and preprocessing the collected visible light image and near infrared spectrum data; extracting a fusion spectrum feature vector of the pre-processed visible light image and the near infrared spectrum data, and generating a feature matrix for material classification; inputting the feature matrix into a lightweight convolutional neural network model deployed in an edge calculation unit, and obtaining a recognition result of the material and form of the bottle; a sorting control instruction is generated based on the recognition result, and motion space state representation is constructed in combination with current state information of the mechanical arm; and inputting the spatial state representation into a path optimization model based on deep reinforcement learning, and outputting and executing an optimal sorting path and a coordination action sequence. According to the method, the precision and robustness of bottle identification can be improved, the recycling efficiency of renewable resources can be improved, and the recycling cost can be reduced.

The invention relates to the technical field of artificial intelligence and deep learning, in particular to a material intelligent classification and deletion detection system based on a deep learning model. In an existing insurance claim settlement process, various kinds of materials need to be submitted, and checking and auditing are mostly completed manually. At present, an insurance company starts to use a deep learning technology to assist auditing, the model scale is huge, a large storage space and strong GPU computing power support are needed, and the cost is high. The system is composed of a material preprocessing module, a hierarchical classification recognition module, a deletion detection module and a result output and prompt module, and all the modules interact through standardized data interfaces; the hierarchical classification and recognition module is used for pre-classifying uploaded materials by using a hierarchical cascade classification architecture and a lightweight convolutional neural network, and quickly pre-classifying the uploaded materials according to scene-class and character-class pictures; the lightweight intelligent classification and deletion detection system provided by the invention gives consideration to high precision and high efficiency, meets actual service requirements, and cannot be replaced by a traditional scheme.

The invention belongs to the technical field of data management. According to the material data cleaning method and system fusing the large language model and the knowledge graph, the knowledge graph stores core knowledge such as an enterprise material classification system, term definition and quality rules in a structured triple form, and a clear standard basis is provided for cleaning operation; accurate structured knowledge anchor points and standardized constraints are provided for the large language model, the illusion of the large language model is effectively inhibited, and it is ensured that the output content conforms to industry specifications; the big language model injects strong natural language understanding, fuzzy semantic analysis and unstructured information extraction capabilities into the knowledge graph, makes up for the flexibility deficiency of the knowledge graph, and deeply fuses the two to jointly solve the key problem of what the text semantics is understood and whether the semantics meets the standard or not. And the material data cleaning precision is improved.

The invention discloses a collaborative optimization method and system for classified disposal and green disassembly of power grid waste and old materials, and relates to the technical field of classified disassembly of materials, and the method comprises the following steps: obtaining multi-source feature data of to-be-disposed power grid waste and old materials; constructing a residual value prediction model based on the multi-source feature data, and outputting a component residual value mapping graph; analyzing the structure information of the target material, and generating a disassembly path decoupling graph in combination with the residual value mapping graph; establishing a dual-objective optimization scheduling model, and solving the scheduling model to generate a green disassembly operation sequence meeting a first constraint condition; and when the disassembling operation sequence is executed, collecting real-time feedback data, dynamically correcting residual value prediction model parameters and scheduling model constraint weights. According to the method, the disassembly path decoupling graph and the disassembly state transition graph are constructed, collaborative representation of the component value and the disassembly topology in the equipment is realized, and the problems of decoupling of residual value evaluation and path planning and lack of green evaluation in a traditional method are effectively solved.

The invention relates to the technical field of power equipment state monitoring and fault diagnosis, and discloses an infrared diagnosis method and system for power transmission and transformation equipment. The method comprises the following steps: acquiring infrared thermal image data of equipment and removing noise by using a filter to obtain a clear image; carrying out pixel-level segmentation by adopting an image segmentation model, determining the boundary of the equipment part, and extracting an initial feature vector which comprises texture and depth heat distribution information; based on the feature sets, judging a temperature abnormal region, and performing material classification in combination with texture information; according to the method, the structural difference of the components is analyzed through the classification model, structural feature description is obtained, the structural features are further combined with the temperature abnormal area, fine feature matching is carried out, and therefore synchronous recognition is achieved, and the accuracy and real-time performance of power transmission and transformation equipment fault diagnosis are improved.

The invention discloses an intelligent examination and approval management method for the housing and construction industry, and the method comprises the steps: collecting examination and approval application data, carrying out the data cleaning, data deduplication and data standardization operation of the examination and approval application data, and obtaining preprocessed data; constructing a data classification model based on a machine learning algorithm, and performing material classification on the preprocessed data by using the data classification model to obtain a material classification result; according to a material classification result, performing primary processing and risk assessment on the preprocessed data by using an intelligent auditing system to obtain a risk score; performing secondary processing on the pre-processed data by using an expert system to obtain a personalized approval scheme; and examining and approving the preprocessed data according to the personalized examination and approval scheme, and storing the risk score and the examination and approval result to an intelligent examination and approval system. The invention relates to the technical field of examination and approval management, and solves the technical problems of low data quality, lack of flexibility and intelligence and the like of an existing residence construction examination and approval system.

The invention discloses a material classification method and device based on an ultraviolet electron spectrum, a medium and a product, and relates to the technical field of material surface science and data analysis. The method comprises the following steps: acquiring ultraviolet light electron energy spectrum data of a target material to be analyzed, and extracting a first spectrum curve from the energy spectrum data; obtaining a conductor characteristic identification result of the target material according to the first spectrum curve; under the condition of determining that the target material does not have the conductor characteristics based on the conductor characteristic identification result, extracting a second spectrum curve in a preset valence band top energy interval extending towards the negative energy direction by taking the Fermi level as a starting point from the ultraviolet light electron spectrum data, and performing multi-dimensional characteristic fusion on the second spectrum curve, generating a semiconductor characteristic identification result of the target material based on the fusion result; and determining a classification result of the target material according to the semiconductor characteristic identification result. According to the scheme, the conductive attributes of the target material can be quickly and accurately classified.

Provided are systems and methods that perform material classification of imagery using generative denoising diffusion models. Traditional material classification systems, which are predominantly based on discriminative models, face issues with generalization and diversity, particularly when encountering new or complex material compositions. Furthermore, these systems often function like black boxes, making it difficult to understand their decision-making processes or identify spurious correlations. The provided systems and methods address these challenges by leveraging the capabilities of diffusion models, which can provide better generalization and more transparent decision-making processes.

The invention discloses an intelligent insurance claim settlement processing method and device based on AI, equipment and a medium. The method comprises the following steps: constructing an insurance product digital model and a claim knowledge base; deploying a multi-terminal intelligent claim settlement channel, and receiving a claim settlement application and a multi-modal claim settlement material submitted by a user through the claim settlement channel; an intelligent material processing engine is adopted to carry out analysis processing on the multi-modal claim settlement material to obtain structured claim settlement data, and analysis processing comprises material classification, image optimization, key information identification and medical text semantic analysis; based on the insurance product digital model, the claim settlement knowledge base and the structured claim settlement data, claim settlement auditing is executed through an AI intelligent auditing decision flow, and a claim settlement auditing result is generated; and the claim settlement audit result and the claim settlement progress information are fed back to the user through the multi-terminal intelligent claim settlement channel, and the claim settlement operation is executed, so that the problems of difficulty in material identification, dependence on manpower in the process, low efficiency and the like in traditional insurance claim settlement can be solved, and intelligent processing of the whole process of insurance claim settlement is realized.

The invention particularly relates to an intelligent garbage classifying and sorting system based on machine vision, and relates to the technical field of intelligent garbage classification and industrial automation. A digital twin construction module; an adaptive control and optimization module; and a predictive maintenance and health management module. According to the method, vision-touch-acoustics multi-mode perception is deeply fused, so that the identification limitation of a traditional single vision sorting system is solved; after the visual identification and the tactile feature are subjected to weighted fusion, the material classification accuracy is improved, and the problems of misjudgment and missed judgment under the scenes of oil contamination, light reflection, form deformation and the like are effectively avoided.

The invention discloses an application material intelligent classification method and system based on data analysis. The method comprises the steps that after an application material text is obtained, keywords are extracted, weights are calculated, and weighted keyword vectors are generated; and mapping the vector to a high-dimensional semantic space, and converting the vector into a multi-dimensional semantic representation vector. And calculating a semantic similarity sequence of the vector and a preset category standard vector, and performing preliminary classification by comparing the semantic similarity sequence with a high similarity threshold. And if the matching is successful, outputting a corresponding type as a final result. If the keyword fails, the keyword weight is evaluated again, and the enhanced semantic representation vector is generated in an enhanced mode. And calculating the similarity between the abnormal material and the standard vector, and screening out the abnormal material according to a low threshold value. And finally, performing semantic proximity analysis and consistency evaluation grouping on the abnormal materials to obtain final classification. According to the method and the system, the classification accuracy of various application materials in the fields of digital public services and the like can be effectively improved.

The invention discloses an electronic material automatic classification method and system based on multistage feature recognition, and relates to the field of material classification, and the method comprises the steps: firstly obtaining unstructured material information, and carrying out the regularization and preprocessing of the information; subsequently, through main category identification and feature lexical element marking, accurate positioning of material core attributes is realized, which exceeds shallow identification of traditional keyword matching, and the defect that complex semantics cannot be processed is effectively overcome. On the basis, structured extraction and analysis are further carried out on the marked features, and the marked features are converted into paradigm features which can be deeply understood by a machine. And finally, through code integration and verification, a standard and unique production code, such as an SPYY. XBBBVVVV structure, is automatically generated. In this way, low-efficiency and error-prone manual operation is replaced, and the fundamental problem that an existing automatic scheme is difficult to deal with description diversity is solved through the powerful analysis capacity and robustness of the manual operation.

The utility model belongs to the technical field of production equipment, and discloses a material receiving, detecting and basketing system which comprises a material distributing unit, a temporary storage unit, a spot check unit, a material arranging unit and a basketing station. The sampling inspection unit comprises a detection platform, a detection module, a classification module, a qualified product platform and a waste collection platform, the material distribution unit selectively places materials on the cache unit or the detection platform, the detection module is used for detecting the materials, and the classification module is used for transferring the materials on the detection platform to the qualified product platform or the waste collection platform; the basket loading station is provided with a material basket, and the material placing unit can move among the temporary storage unit, the qualified product platform and the basket loading station and is used for picking up materials of the temporary storage unit and the qualified product platform and transferring the materials to the material basket. According to the system, the working procedures of detecting, classifying, basketing and the like of products can be rapidly achieved after the products are discharged, the collaboration among the mechanisms is improved, the basketing efficiency is improved, the layout can be more reasonable, and cost reduction and efficiency improvement are achieved.

The invention discloses a building exterior facing apparent damage survey method based on an unmanned aerial vehicle, and the method comprises the steps: enabling a trained damage detection instance segmentation deep learning model and a trained material classification neural network model to be deployed in an unmanned aerial vehicle, and carrying out the deployment of the unmanned aerial vehicle; the unmanned aerial vehicle is used for routing inspection route planning, close routing inspection collection, damage detection and segmentation, pixel size calculation, facing material identification, spatial parameter calculation and damage evaluation treatment, so that the apparent damage of the exterior facing of the building is intelligently detected and the damage degree and grade are evaluated through the two dimensions of damage and material, and a treatment suggestion is given. The method realizes closed-loop management of detection-evaluation-disposal, and has the advantages of high detection efficiency and high precision.

A metal material classification and recovery system, comprising: a conveying unit (300) used for conveying a material to be classified (407) to a first chute (302) provided at the tail end of the conveying unit (300), the first chute (302) comprising at least one channel (3021), and the channel (3021) being used for guiding said material (407) to freely fall in a set direction; a sensor array (400) comprising sensors (401) in one-to-one correspondence to the channels (3021), each sensor (401) being distributed in the same horizontal direction below the first chute (302), and the sensor (401) being used for detecting whether said material (407) falling from the corresponding channel (3021) belongs to a target material type; and a classification unit (500) used for blowing said material (407) into a corresponding material frame when said material (407) is detected to belong to the target material type. Also provided is a metal material classification and recovery method.

The invention provides a carbon emission intelligent calculation method and device, equipment and a medium, and the method comprises the steps: obtaining the raw material purchasing information of a target supplier, covering the material classification, subclass identification and purchasing measurement, querying a predefined rule base based on the material classification information, determining the carbon emission weight calculation type corresponding to each material, and calculating the carbon emission weight according to the carbon emission weight calculation type. Querying a historical carbon emission intensity record of the target supplier, if no record exists, generating a historical reference coefficient by adopting the reference value of the same-industry and same-scale enterprise, inputting the weight calculation type and the historical reference coefficient into a preset machine learning model, and outputting an emission coefficient and confidence; and finally, carrying out correlation calculation on the emission coefficient of which the confidence exceeds a preset threshold and purchase measurement to obtain the carbon emission of the target commodity. According to the method, through combination of the emission coefficient and the machine learning model, intelligent and differentiated accounting of the carbon emission is realized, the method is suitable for multi-scene supply chain carbon footprint management, and the accounting efficiency and accuracy are significantly improved.

The invention discloses an industrial flexible object detection method based on machine vision, and relates to the technical field of industrial detection, and the method comprises the steps: combining a texture feature vector with an extracted geometric shape feature vector to generate a visual feature vector, extracting a global touch feature based on a pre-trained ViT-B / 16 model, and carrying out the detection of an industrial flexible object. The method comprises the following steps: extracting position and speed feature vectors through a CSPDarknet network architecture based on a radar mask pattern, and generating a comprehensive feature vector through a Transform model and a modal weight; a radar mask pattern is efficiently generated through a depth mapping conversion network, multi-modal features are extracted, a comprehensive feature vector is generated through model adaptive fusion, model parameters are dynamically adjusted through smooth features and online adaptive optimization, the calculation complexity and the marking cost are reduced, the robustness to illumination change and deformation scenes is improved, and the method is suitable for large-scale popularization and application. And the detection precision of the bounding box, the material classification and the deformation state probability of the flexible object is improved.

The invention discloses a multi-class material sorting method and system based on hyperspectrum, a medium and a product, and relates to the technical field of intelligent sorting. According to the method, the sorting system collects the hyperspectral data of different types of materials on the conveying belt, and the characteristic wavelength point with the highest contribution degree to material classification is selected by combining the spectral response differences of the different types of materials at a plurality of wavelength points; and the transverse center positions and the longitudinal center positions of the to-be-sorted materials and the characteristic spectral response values at the characteristic wavelength points are extracted, a characteristic data set is generated and input into the recognition model for recognition, the sorting rule database is inquired according to the recognition result to determine the sorting parameters corresponding to the to-be-sorted materials, and finally accurate sorting of the materials is achieved. Compared with a traditional sorting method which only depends on RGB images, the method can capture spectral characteristics of the surfaces of the materials, is not affected by surface pollution, color fading, light reflection and shadow, can distinguish materials with similar appearances but different materials, and greatly improves the sorting accuracy and stability.

The invention discloses an image recognition and classification system and method based on machine learning, and relates to the field of industrial image processing, and the method comprises the steps: collecting image data of an industrial product, and marking related information; the method comprises the following steps: detecting an acquired industrial product image through a self-adaptive method, removing noise in the image, and classifying industrial product materials based on feature extraction; dynamically adjusting the weight of a defect detection task by sharing a CNN backbone network according to the importance of a product type; constructing a multi-task model to evaluate the severity of the defect; constructing a physical parameter calculation function, setting a score by combining a multi-task model output result, performing weighted fusion on the function and the model result, and classifying the severity of product defects according to a fusion result; through adaptive noise removal and material classification, adaptability to a complex industrial environment is enhanced, and through combination of a multi-task model and physical parameters, comprehensive evaluation of defects from multiple dimensions is realized, and classification precision is improved.

The invention relates to a material reserve management method and system, and belongs to the technical field of electric power material management. The method comprises the steps of data collection and analysis, material classification and warehouse classification, material distribution through an optimization model, obtaining of information of objects to be stored, material storage and label generation, sending of a goods taking request, goods taking and transportation, delivery and information updating. According to the invention, the efficiency of material management is improved, the inventory cost is reduced, the reliability of a power system is enhanced, the optimal utilization of resources is promoted, and scientific data support is provided for material management and allocation.

The invention provides an obstacle recognition and laser obstacle clearance method for a power transmission line, and the method comprises the steps: collecting the spectral data of an obstacle through multispectral imaging, carrying out the material classification through a convolutional neural network, analyzing the form of the obstacle in combination with a depth image, and planning an obstacle clearance track through a dynamic planning algorithm. Laser obstacle clearing parameters are dynamically adjusted according to environmental factors, accurate recognition and classification of different types of obstacles such as metal, branches and bird nests are achieved, the optimal obstacle clearing track and parameters can be generated in a self-adaptive mode, real-time monitoring and adjustment are conducted in the obstacle clearing process, the intelligence and accuracy of obstacle clearing of the power transmission line are effectively improved, and the working efficiency is improved. And safe and stable operation of a power grid is ensured. According to the technical scheme of the invention, advanced technologies such as multispectral analysis, deep learning and three-dimensional imaging are fused, the whole-process intelligentization of obstacle removal of the power transmission line is realized, and the method has important practical value.

The invention provides a kitchen waste weighing method, device and equipment and a storage medium, belongs to the field of data processing, and is applied to electronic equipment in a collection and transportation system, the collection and transportation system comprises the electronic equipment and an image acquisition device installed on a waste collection and transportation vehicle, and the electronic equipment is in communication connection with the image acquisition device. The method comprises the following steps: acquiring an image sequence acquired by an image acquisition device on a garbage collection site; according to the image sequence, determining that the garbage can appearing in the image sequence meets a can loading condition; analyzing at least part of images in the image sequence by utilizing a can volume identification model, and determining the volume of the kitchen waste in the garbage can; analyzing at least part of images in the image sequence by using a material classification model, and determining the material of the kitchen waste in the garbage can; and determining the weight of the kitchen waste in the garbage can based on the volume and the material. According to the scheme, the weighing accuracy of the kitchen waste can be improved.

The invention provides an automatic detection and sorting system for thread protectors, and relates to the technical field of detection and sorting of petroleum and natural gas pipeline accessories, and the system comprises an acquisition device which carries out image acquisition, size measurement and spectrum detection on the thread protectors to form a to-be-detected data set; the to-be-detected data set comprises appearance image data, size parameter data and material spectrum data. According to the automatic detection and sorting system for the thread protectors, the thread protectors are comprehensively detected through multiple means such as image acquisition, size measurement and spectrum detection, the appearance defects, geometric parameters and material classification characteristics of the thread protectors are accurately recognized, and the detection precision is remarkably improved. And through application of intelligent algorithms such as the convolutional neural network, the system can automatically judge the detection result, so that automatic sorting is realized, manual intervention is effectively reduced, and the sorting efficiency is improved.

The invention discloses a high-energy laser electrified deicing method and system for a post insulator of a transformer substation based on material classification, and the method comprises the steps: classifying the post insulator of the transformer substation according to materials, and adjusting the parameters of a high-energy laser according to the number threshold values of photons in unit area corresponding to different materials; safe and efficient electrified deicing of the post insulator of the transformer substation is realized, and damage to an insulator material caused by improper laser energy is avoided.

The utility model relates to the technical field of building construction, and discloses a building construction site material classified storage device which comprises four supporting columns, a plurality of storage plates are connected to the outer portions of the four supporting columns in a sliding mode, a plurality of sliding grooves are formed in the supporting columns, sliding rods are fixedly connected to the interiors of the sliding grooves, and the sliding rods are connected with the storage plates in a sliding mode. A first spring sleeves the outer portion of the sliding rod, a sliding block is slidably connected to the outer portion of the sliding rod, a limiting block is fixedly connected to the outer portion of the sliding block, a plurality of mounting grooves are formed in the supporting column, ejecting blocks are slidably connected to the inner portions of the mounting grooves, and elastic assemblies for ejecting the ejecting blocks are fixedly connected to the inner portions of the mounting grooves. The bottom of the supporting column is fixedly connected with a moving assembly capable of rotating. According to the building construction site material storage device, the height between the storage trays is adjusted, flexible adjustment is conducted according to the sizes and storage requirements of different building construction site materials, and stable storage of objects is guaranteed.