140 results about "Material classification" patented technology

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 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 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.

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 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 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 present application relates to an information processing method, device, computing equipment and medium. The present application obtains first difference information representing the difference between the standard product cost of a product component of a target product and the first theoretical product cost of the product component, and obtains second difference information representing the difference between the standard product cost of the product component and the second theoretical product cost of the product component, determines difference adjustment information based on the first difference information and the second difference information, adjusts the first cost information obtained by the material ledger module based on the difference adjustment information to obtain second cost information that is closer to the actual cost of the target product, improves the accuracy of the calculated actual component cost, and reduces the occurrence of distortion of the calculated actual component cost.

The utility model relates to the technical field of signal line storage, and particularly discloses an efficient material classifying device which comprises a base and a supporting column fixedly connected to the base, the supporting column is rotationally connected with a bearing plate through a limiting piece, and the bearing plate is fixedly connected with a plurality of sets of mounting frames. A bearing frame is slidably connected to the interior of each mounting frame; an extrusion assembly for extruding the wire harnesses is further arranged between the mounting frame and the bearing frame, so that the wire harnesses in the bearing frame are in a tidy state; and an unlocking part is also arranged between the bearing frame and the mounting frame. The bearing frame is driven to slide towards the outer side of the mounting frame, in the sliding stroke of the bearing frame, the extrusion assembly is driven to slide upwards to be away from the wire harness in the bearing frame through the unlocking part, at the moment, the extrusion assembly unlocks the wire harness body in the bearing frame, the wire harness in the bearing frame can be easily taken, and the wire harness can be taken without unlocking the extrusion assembly; and the wire harness taking efficiency is improved.

This utility model relates to the field of auxiliary equipment for construction engineering, and discloses a portable material classification, storage, and transportation box for construction engineering. The box includes a storage box with two fixed plates fixedly connected to its bottom inner wall. A motor is fixedly connected to the inner wall of one of the fixed plates, and a gear is fixedly connected to the drive end of the motor. Connecting rods are rotatably connected inside both fixed plates, and a gear is fixedly connected to the outer wall of one of the connecting rods. Transmission plates are fixedly connected to the outer walls of both connecting rods, and connecting rods are fixedly connected to the inner walls of both transmission plates. This utility model reduces the corrosion of the box material by rainwater and acidic or alkaline liquids such as construction site concrete slurry, prevents aluminum alloy oxidation and steel plate rusting, maintains structural strength, and the sealed structure reduces the leakage of volatile gases while preventing the intrusion of external corrosive gases, ensuring the chemical stability of the materials.

This application discloses a method and apparatus for material classification, a machine-readable storage medium, and an electronic device. The classification method includes: traversing model files to establish a first dataset, a second dataset, a third dataset, a fourth dataset, a fifth dataset, a sixth dataset, and a seventh dataset; for each object in each of the third, fourth, fifth, and sixth datasets, using an AI large language model, determining the corresponding recommended material type, and assigning the determined recommended material type to the object's material classification attribute. This achieves automated material classification.

The present application relates to the technical field of industrial vision detection and intelligent manufacturing, and in particular provides a papermaking wood grading evaluation method based on 3D vision and high-resolution imaging fusion. The method comprises raw material feeding and pre-screening, and collecting front and back multi-modal information to realize heterogeneous data archiving, establish an initial digital archive of the target physical form; camera alignment and foreground segmentation are performed, and material categories and associated metadata are identified to obtain offline grading evaluation. Through hardware synchronization and algorithm cooperation, the method realizes automatic collection of front and back of wood, calculation of three-dimensional geometric parameters, material classification and grading evaluation, significantly improving detection accuracy, reviewability and automation level.

The invention relates to the technical field of polishing, in particular to a remote controller button burr polishing device. Comprising a first supporting frame, a first spring is connected to the first supporting frame, a first mounting frame is connected to the first spring, a first vibration motor and a loading frame are mounted on the first mounting frame, a material cleaning mechanism is arranged in the loading frame and used for pushing out materials in the loading frame, and a vibration mechanism is arranged on the side face of the first supporting frame. The vibrating mechanism is connected with a second connecting frame and a collecting hopper, and the vibrating mechanism is used for driving the second connecting frame to vibrate. By arranging the vibrating mechanism, the material cleaning mechanism, the material conveying mechanism and the filtering mechanism, automatic separation of workpieces, grinding materials and burr chippings after remote controller buttons are ground is achieved, a traditional manual sorting mode is replaced, the labor cost is greatly reduced, the overall production efficiency is improved, and the remote controller button grinding machine is particularly suitable for a large-batch production scene; and moreover, high-precision material classification is realized, and the problems of missing sorting and mistaken sorting are avoided.

The invention aims to provide a gas turbine-oriented production process intelligent material preparation method, which belongs to the field of gas turbine material preparation, and comprises the following steps: selecting material list parameter information, and generating a material preparation process MP parameter list of a product; circularly calculating the consumption quota CQ and the raw material project inventory RMPI of the materials in the material list MP parameter list, and judging the raw material state MS; carrying out material inventory state MIS inspection, pre-adjusting a bill of materials, and distributing material code values participating in material preparation; carrying out inventory WBS change on the materials; using a preset alternative material classifier to select alternative materials to complete a material preparation task, and completing material mapping of both sides of the alternative materials; the materials with the size capable of being directly used by the blank are rapidly positioned in the full-amount material database, and the blank receiving task is directly executed after the nuclear materials pass through the full-amount material database. According to the invention, automatic execution of the material preparation process of gas turbine product production, real-time sensing of the material state and accurate calculation of the demand can be realized.

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 utility model relates to the technical field of filter element conveying, in particular to a filter element blank conveying device which comprises a conveying belt, a fixing frame is fixedly installed on one side of the conveying belt, and a visual scanner and a classification conveying mechanism are fixedly installed on one side of the fixing frame. The classification conveying mechanism used for conducting good and bad material classification conveying on the filter elements is arranged on one side of the conveying belt, the classification conveying mechanism comprises a conveying frame fixedly installed on one side of the conveying belt, and a classification assembly is arranged in the conveying frame; during use, the conveying belt is used for conveying filter elements, when the elements pass through the visual scanner, the elements can be scanned, recognized and judged to be good products or bad materials, the classification assembly can distribute the filter elements entering the conveying frame according to the recognition result, the good products or the bad materials are guided to different positions, mixed loading is avoided, and the production efficiency is improved. A storage assembly is arranged on the other side of the conveying frame and used for storing bad filter elements and good filter elements.

The present application relates to the technical field of image recognition, in particular to a mine belt intelligent foreign matter identification method and system based on machine vision, wherein the mine belt intelligent foreign matter identification method based on machine vision comprises: acquiring an image of coal material transported by a mine belt, identifying and segmenting a foreign matter area; determining the material type of the foreign matter according to the brightness distribution parameters of the foreign matter area; for the stick-shaped foreign matter prone to misidentification, calculating the alignment deviation index of the reflection area and the boundary of the foreign matter area, and calculating the dissimilarity of the reflection area boundary of the stick-shaped foreign matter in the front and rear frame images; determining the stick-shaped foreign matter discrimination index based on the alignment deviation index and the dissimilarity, and distinguishing the material type of the stick-shaped foreign matter; and performing corresponding processing on the foreign matter according to the distinguished material type. The above method constructs a lightweight identification system independent of deep neural networks, reduces the dependence on large-scale labeled data and hardware computing power requirements, and realizes accurate material classification of mine belt foreign matters.

The invention relates to the technical field related to storage management, in particular to an intelligent warehouse management system and method, and the method comprises the steps: determining operation influence factors according to historical warehouse data, carrying out the parameter iteration optimization of a warehouse scheduling model corresponding to a warehouse operation detection index till the maximum number of iterations is reached, and determining a storage scheduling scheme list according to the material classification database, the storage layout database and the equipment scheduling database. The technical problems of low material positioning precision and insufficient inventory accuracy caused by separation of storage partition planning and an AGV carrying path and difficulty in effective consideration of the space utilization rate and the warehouse-in and warehouse-out efficiency are solved, the material storage channel, the material carrying channel and the inventory monitoring channel are integrated through the warehouse scheduling model, the matching degree of the storage planning and the AGV path is improved, and the accuracy of the storage planning and the AGV path is improved. The continuous parameter iteration enables the warehouse scheduling model to adapt to order fluctuation and equipment state change in real time, and improves the material positioning precision and inventory accuracy.

This invention discloses a method, apparatus, system, and storage medium for simulating modulation TOF camera correction. First, a light intensity threshold is calculated as the depth error classification standard. The light intensity threshold with the largest standard deviation of the depth error is selected as the depth error oscillation critical point. A light intensity-distance lookup table is established based on the light intensity-distance dataset to correct high-intensity depth data. A light intensity-depth random forest nonlinear model is established, and the hyperparameters of the model are optimized using a particle swarm optimization algorithm for low-intensity depth data. Next, a material correction classification model is constructed. For light scattering errors caused by materials in the measurement scene, the LDA algorithm is used to extract effective features and establish a material-depth distortion dataset. Finally, during real-time acquisition, the trained material correction classification model is used to classify the materials of the acquired objects. Based on the material classification results, the depth data is corrected using the material-depth distortion dataset to obtain a high-quality depth image.

The invention provides a sintering mixing water adding control method and system based on material tracking and material classification and a storage medium. The method comprises the steps that tracking queues Queeni are established for n discharging points of a batching system respectively; the materials participating in production are divided into K types according to types, and the water adding proportionality coefficient of each type of materials is set; acquiring the material variation of each type of materials at the inlet of the mixer in the previous calculation period and the current calculation period; according to the target moisture content of the mixture, the material variable quantity of each type of materials and the water adding proportionality coefficient, the water adding amount needed by each type of materials in the current calculation period is calculated; and obtaining the total water adding amount required in the current calculation period. According to the control method, the tracking queue is established for each discharging point, so that the total material flow of various materials entering the inlet of the mixing machine can be timely and accurately obtained, and the water adding amount can be timely and accurately calculated along with the fluctuation of the materials.

The invention relates to intelligent classification of coal mining materials. The method comprises the following steps: acquiring multi-source data associated with coal mining, and performing feature fusion on the multi-source data to obtain a three-dimensional association model; the three-dimensional association model is an association model for equipment information, material information and equipment maintenance information of coal mining, generating an association map by using the three-dimensional association model based on a pre-constructed unified data category model, and inputting the association map into the trained map neural network model to obtain a map neural network model; and obtaining a material classification result output by the graph neural network model. According to the scheme, the material management efficiency and the intelligent level are improved.